Artificial Intelligence-Driven Cancer Diagnostics: Enhancing Radiology and Pathology through Reproducibility, Explainability, and Multimodality

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21492 · Cited by 84,109 · see citing works

MizAR 60 for Mizar 50

Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648 · see citing works

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

Unpaired image-to-image translation using cycle-consistent adversarial networks

Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602 · see citing works

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.11929 · Cited by 21,793 · see citing works

Generative adversarial networks

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020) · Communications of the ACM · doi:10.1145/3422622 · Cited by 18,384 · see citing works

End-to-End Object Detection with Transformers

Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_13 · Cited by 18,285 · see citing works

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Tan, M., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1905.11946 · Cited by 17,072 · see citing works

A simple framework for contrastive learning of visual representations

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020) · arXiv · doi:10.48550/arxiv.2002.05709 · Cited by 10,857 · see citing works

Attention U-Net: Learning Where to Look for the Pancreas

Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., et al. (2018) · arXiv · doi:10.48550/arxiv.1804.03999 · Cited by 5,458 · see citing works

Generative Adversarial Networks

Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.2661 · Cited by 4,477 · see citing works

The future of digital health with federated learning

Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 3,119 · see citing works

Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930 · see citing works

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799 · see citing works

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes

Li, X., Chen, H., Qi, X., Dou, Q., Fu, C., & Heng, P. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2845918 · Cited by 2,530 · see citing works

Data-efficient and weakly supervised computational pathology on whole-slide images

Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Chen, R. J., Barbieri, M., & Mahmood, F. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00682-w · Cited by 2,282 · see citing works

Explainability for artificial intelligence in healthcare: a multidisciplinary perspective

the Precise4Q consortium, Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01332-6 · Cited by 2,203 · see citing works

Cell Detection with Star-Convex Polygons

Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00934-2_30 · Cited by 1,826 · see citing works

Towards a general-purpose foundation model for computational pathology

Chen, R. J., Ding, T., Lu, M. Y., Williamson, D. F. K., Jaume, G., Song, A. H., Chen, B., Zhang, A., Shao, D., Shaban, M., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02857-3 · Cited by 1,560 · see citing works

Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

Kather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0462-y · Cited by 1,458 · see citing works

Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images

Graham, S., Vu, Q. D., Raza, S. E. A., Azam, A. S., Tsang, Y. W., Kwak, J. T., & Rajpoot, N. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101563 · Cited by 1,445 · see citing works

Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging

Najjar, R. (2023) · Diagnostics · doi:10.3390/diagnostics13172760 · Cited by 961 · see citing works

A whole-slide foundation model for digital pathology from real-world data

Xu, H., Usuyama, N., Bagga, J., Zhang, S., Rao, R., Naumann, T., Wong, C., Gero, Z., González, J., Gu, Y., et al. (2024) · Nature · doi:10.1038/s41586-024-07441-w · Cited by 921 · see citing works

Deep learning in histopathology: the path to the clinic

van der Laak, J., Litjens, G., & Ciompi, F. (2021) · Nature Medicine · doi:10.1038/s41591-021-01343-4 · Cited by 902 · see citing works

Federated learning for smart healthcare: A survey

Nguyen, D. C., Pham, Q.-V., Pathirana, P. N., Ding, M., Seneviratne, A., Lin, Z., Dobre, O., & Hwang, W.-J. (2023) · ACM Computing Surveys · doi:10.1145/3501296 · Cited by 827 · see citing works

AI in Medical Imaging Informatics: Current Challenges and Future Directions

Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713 · see citing works

Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study

Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., van der Laak, J., Hulsbergen-van de Kaa, C., & Litjens, G. (2020) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30739-9 · Cited by 705 · see citing works

Harnessing multimodal data integration to advance precision oncology

Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J., & Shah, S. P. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-021-00408-3 · Cited by 662 · see citing works

A survey on active learning and human-in-the-loop deep learning for medical image analysis

Budd, S., Robinson, E. C., & Kainz, B. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102062 · Cited by 606 · see citing works

A foundation model for clinical-grade computational pathology and rare cancers detection

Vorontsov, E., Bozkurt, A., Casson, A., Shaikovski, G., Zelechowski, M., Severson, K., Zimmermann, E., Hall, J., Tenenholtz, N., Fusi, N., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03141-0 · Cited by 572 · see citing works

Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare

Williamson, S. M., & Prybutok, V. (2024) · Applied Sciences · doi:10.3390/app14020675 · Cited by 567 · see citing works

Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge

Bulten, W., Kartasalo, K., Chen, P.-H. C., Ström, P., Pinckaers, H., Nagpal, K., Cai, Y., Steiner, D. F., van Boven, H., Vink, R., et al. (2022) · Nature Medicine · doi:10.1038/s41591-021-01620-2 · Cited by 559 · see citing works

External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review

Yu, A. C., Mohajer, B., & Eng, J. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.210064 · Cited by 455 · see citing works

Primary osteogenic sarcoma: pathologic aspects in 20 patients after treatment with chemotherapy en bloc resection, and prosthetic bone replacement.

Ag, H., Rosen, G., & Marcove, R. C. (1977) · PubMed · Cited by 396 · see citing works

Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer

Vanguri, R. S., Luo, J., Aukerman, A. T., Egger, J. V., Fong, C. J., Horvat, N., Pagano, A., Araujo-Filho, J. D. A. B., Geneslaw, L., Rizvi, H., et al. (2022) · Nature Cancer · doi:10.1038/s43018-022-00416-8 · Cited by 395 · see citing works

Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning

Echle, A., Grabsch, H. I., Quirke, P., van den Brandt, P. A., West, N. P., Hutchins, G. G. A., Heij, L. R., Tan, X., Richman, S. D., Krause, J., et al. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.06.021 · Cited by 376 · see citing works

Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge

Wouter, B., Kimmo, K., Cameron, C. P.-H., Peter, S., Hans, P., Kunal, N., Yuannan, C., Steiner, D. F., van Boven, H., Robert, V., et al. (2022) · UTUPub (University of Turku) · Cited by 375 · see citing works

Deep learning-enabled virtual histological staining of biological samples

Bai, B., Yang, X., Li, Y., Zhang, Y., Pillar, N., & Özcan, A. (2023) · Light Science & Applications · doi:10.1038/s41377-023-01104-7 · Cited by 352 · see citing works

Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools

Ranjbarzadeh, R., Caputo, A., Tırkolaee, E. B., Ghoushchi, S. J., & Bendechache, M. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106405 · Cited by 336 · see citing works

Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images

Khosravi, P., Kazemi, E., Imielinski, M., Elemento, O., & Hajirasouliha, I. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2017.12.026 · Cited by 334 · see citing works

Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks

Wei, J., Tafe, L. J., Linnik, Y., Vaickus, L. J., Tomita, N., & Hassanpour, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-40041-7 · Cited by 314 · see citing works

Cell Detection with Star-Convex Polygons

Schmidt, U., Weigert, M., Broaddus, C., & Myers, E. W. (2018) · MPG.PuRe (Max Planck Society) · Cited by 295 · see citing works

GANs for Medical Image Synthesis: An Empirical Study

Skandarani, Y., Jodoin, P., & Lalande, A. (2023) · Journal of Imaging · doi:10.3390/jimaging9030069 · Cited by 292 · see citing works

Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks

Tellez, D., Balkenhol, M., Otte-Höller, I., van de Loo, R., Vogels, R., Bult, P., Wauters, C., Vreuls, W., Mol, S., Karssemeijer, N., et al. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2820199 · Cited by 288 · see citing works

Computational pathology: Challenges and promises for tissue analysis

Fuchs, T. J., & Buhmann, J. M. (2011) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2011.02.006 · Cited by 267 · see citing works

Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges

Chen, Z., Pawar, K., Ekanayake, M. M., Pain, C. D., Zhong, S., & Egan, G. F. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00721-9 · Cited by 238 · see citing works

Adversarial Stain Transfer for Histopathology Image Analysis

BenTaieb, A., & Hamarneh, G. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2781228 · Cited by 230 · see citing works

Development and Validation of a Deep Learning Algorithm for Gleason Grading of Prostate Cancer From Biopsy Specimens

Nagpal, K., Foote, D., Tan, F. E., Liu, Y., Chen, P.-H. C., Steiner, D. F., Manoj, N. S., Olson, N. H., Smith, J. L., Mohtashamian, A., et al. (2020) · JAMA Oncology · doi:10.1001/jamaoncol.2020.2485 · Cited by 223 · see citing works

Whole slide imaging equivalency and efficiency study: experience at a large academic center

Hanna, M. G., Reuter, V. E., Hameed, M., Tan, L. K., Chiang, S., Sigel, C., Hollmann, T. J., Giri, D. D., Samboy, J., Moradel, C., et al. (2019) · Modern Pathology · doi:10.1038/s41379-019-0205-0 · Cited by 215 · see citing works

Artificial intelligence: A critical review of applications for lung nodule and lung cancer

de Margerie‐Mellon, C., & Chassagnon, G. (2022) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2022.11.007 · Cited by 213 · see citing works

Radiomics and artificial intelligence for precision medicine in lung cancer treatment

Chen, M., Copley, S. J., Viola, P., Lu, H., & Aboagye, E. O. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.05.004 · Cited by 213 · see citing works

Deep learning in generating radiology reports: A survey

Monshi, M. M. A., Poon, J., & Chung, Y. Y. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101878 · Cited by 194 · see citing works

Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies

Raciti, P., Sue, J., Ceballos, R., Godrich, R., Kunz, J. D., Kapur, S., Reuter, V., Grady, L., Kanan, C., Klimstra, D. S., et al. (2020) · Modern Pathology · doi:10.1038/s41379-020-0551-y · Cited by 186 · see citing works

Artificial intelligence assistance significantly improves Gleason grading of prostate biopsies by pathologists

Bulten, W., Balkenhol, M. C. A., Belinga, J.-J. A., Brilhante, A., Çakır, A., Egevad, L., Eklund, M., Farré, X., Geronatsiou, K., Molinié, V., et al. (2020) · Modern Pathology · doi:10.1038/s41379-020-0640-y · Cited by 183 · see citing works

Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification

Ghahremani, P., Li, Y., Kaufman, A. E., Vanguri, R. S., Greenwald, N. F., Angelo, M., Hollmann, T. J., & Nadeem, S. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00471-x · Cited by 145 · see citing works

Robust Histopathology Image Analysis: To Label or to Synthesize?

Hou, L., Agarwal, A., Samaras, D., Kurç, T., Gupta, R., & Saltz, J. H. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00873 · Cited by 139 · see citing works

A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer

Amgad, M., Hodge, J. M., Elsebaie, M. A. T., Bodelon, C., Puvanesarajah, S., Gutman, D. A., Siziopikou, K. P., Goldstein, J. A., Gaudet, M. M., Teras, L. R., et al. (2024) · Nature Medicine · doi:10.1038/s41591-023-02643-7 · Cited by 137 · see citing works

Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes

Gandhi, Z., Gurram, P., Amgai, B., Lekkala, S. P., Lokhandwala, A., Manne, S., Mohammed, A. S., Koshiya, H., Dewaswala, N., Desai, R., et al. (2023) · Cancers · doi:10.3390/cancers15215236 · Cited by 132 · see citing works

Validation of MSIntuit as an AI-based pre-screening tool for MSI detection from colorectal cancer histology slides

Saillard, C., Dubois, R., Tchita, O., Loiseau, N., Garcia, T., Adriansen, A., Carpentier, S., Reyre, J., Enéa, D., von Loga, K., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42453-6 · Cited by 126 · see citing works

Artificial intelligence in lung cancer: current applications and perspectives

Chassagnon, G., de Margerie‐Mellon, C., Vakalopoulou, M., Marini, R., Hoang-Thi, T.-N., Revel, M., & Soyer, P. A. (2022) · Japanese Journal of Radiology · doi:10.1007/s11604-022-01359-x · Cited by 125 · see citing works

A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology–Radiology Fusion

Khosravi, P., Lysandrou, M., Eljalby, M., Li, Q., Kazemi, E. M., Zisimopoulos, P., Sigaras, A., Brendel, M., Barnes, J., Ricketts, C., et al. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27599 · Cited by 121 · see citing works

Deep learning assisted mitotic counting for breast cancer

Balkenhol, M. C. A., Tellez, D., Vreuls, W., Clahsen, P. C., Pinckaers, H., Ciompi, F., Bult, P., & van der Laak, J. A. W. M. (2019) · Laboratory Investigation · doi:10.1038/s41374-019-0275-0 · Cited by 119 · see citing works

Deep Multi-Magnification Networks for multi-class breast cancer image segmentation

Ho, D. J., Yarlagadda, D. V. K., D’Alfonso, T. M., Hanna, M. G., Grabenstetter, A., Ntiamoah, P., Brogi, E., Tan, L. K., & Fuchs, T. J. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101866 · Cited by 114 · see citing works

Overview of Artificial Intelligence in Breast Cancer Medical Imaging

Zheng, D., He, X., & Jing, J. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12020419 · Cited by 113 · see citing works

Generative AI for brain image computing and brain network computing: a review

Gong, C., Jing, C., Chen, X., Pun, C. M., Huang, G., Saha, A., Nieuwoudt, M., Li, H.-X., Hu, Y., & Wang, S. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1203104 · Cited by 111 · see citing works

Generative Adversarial Networks in Digital Pathology and Histopathological Image Processing: A Review

Jose, L., Liu, S., Russo, C., Nadort, A., & Ieva, A. D. (2021) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_103_20 · Cited by 105 · see citing works

Advances in Computer and Electrical Engineering

(2017) · Advances in computer and electrical engineering book series · doi:10.4018/acee · Cited by 102 · see citing works

Artificial Intelligence-Based Image Enhancement in PET Imaging

Liu, J., Malekzadeh, M., Mirian, N., Song, T.-A., Liu, C., & Dutta, J. (2021) · PET Clinics · doi:10.1016/j.cpet.2021.06.005 · Cited by 98 · see citing works

Advances of AI in image-based computer-aided diagnosis: A review

Yeasmin, M. N., Amin, M. A., Joti, T. J., Aung, Z., & Azim, M. A. (2024) · Array · doi:10.1016/j.array.2024.100357 · Cited by 97 · see citing works

Cross-scale multi-instance learning for pathological image diagnosis

Deng, R., Cui, C., Remedios, L. W., Bao, S., Womick, R. M., Chiron, S., Li, J., Roland, J. T., Lau, K. S., Liu, Q., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103124 · Cited by 95 · see citing works

Artificial Intelligence Applications in Breast Imaging: Current Status and Future Directions

Taylor, C. R., Monga, N., Johnson, C., Hawley, J. R., & Patel, M. J. (2023) · Diagnostics · doi:10.3390/diagnostics13122041 · Cited by 94 · see citing works

Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images

Huang, Z., Shao, W., Han, Z., Alkashash, A. M., la Sancha, C. D., Parwani, A. V., Nitta, H., Hou, Y., Wang, T., Salama, P., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00352-5 · Cited by 90 · see citing works

Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: A multicentre retrospective diagnostic study

Tang, D., Wang, L., Ling, T., Lv, Y., Ni, M., Zhan, Q., Fu, Y., Zhuang, D., Guo, H., Dou, X., et al. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.103146 · Cited by 84 · see citing works

Artificial intelligence applications in prostate cancer

Baydoun, A., Jia, A. Y., Zaorsky, N. G., Kashani, R., Rao, S. K., Shoag, J. E., Vince, R., Bittencourt, L. K., Zuhour, R. J., Price, A. T., et al. (2023) · Prostate Cancer and Prostatic Diseases · doi:10.1038/s41391-023-00684-0 · Cited by 84 · see citing works

Region of interest (ROI) selection using vision transformer for automatic analysis using whole slide images

Hossain, M. S., Shahriar, G. M., Syeed, M. M. M., Uddin, M. F., Hasan, M., Shingla, S., & Advani, S. H. (2023) · Scientific Reports · doi:10.1038/s41598-023-38109-6 · Cited by 82 · see citing works

Multi-modal transformer architecture for medical image analysis and automated report generation

Raminedi, S., Shridevi, S., & Won, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-69981-5 · Cited by 74 · see citing works

A hybrid end-to-end learning approach for breast cancer diagnosis: convolutional recurrent network

Aslan, M. F. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108562 · Cited by 52 · see citing works

The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma

Pan, X., AbdulJabbar, K., Coelho‐Lima, J., Grapa, A.-I., Zhang, H., Cheung, A. H. K., Baena, J., Karasaki, T., Wilson, C., Sereno, M., et al. (2024) · Nature Cancer · doi:10.1038/s43018-023-00694-w · Cited by 51 · see citing works

Automatic AI-based contouring of prostate MRI for online adaptive radiotherapy

Nachbar, M., Russo, M. L., Gani, C., Boeke, S., Wegener, D., Paulsen, F., Zips, D., Roque, T., Nikos Paragios, A., & Thorwarth, D. (2023) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2023.05.001 · Cited by 50 · see citing works

AI improves accuracy, agreement and efficiency of pathologists for Ki67 assessments in breast cancer

Dy, A., Nguyen, N.-N. J., Meyer, J. M., Dawe, M., Shi, W., Androutsos, D., Fyles, A., Liu, F., Done, S. J., & Khademi, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-51723-2 · Cited by 49 · see citing works

Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases

Retámero, J. A., Gulturk, E., Bozkurt, A., Liu, S. T., Gorgan, M., Moral, L., Horton, M., Parke, A., Malfroid, K., Sue, J., et al. (2024) · The American Journal of Surgical Pathology · doi:10.1097/pas.0000000000002248 · Cited by 48 · see citing works

A fully automated and explainable algorithm for predicting malignant transformation in oral epithelial dysplasia

Shephard, A., Bashir, R. M. S., Mahmood, H., Jahanifar, M., Minhas, F., Raza, S. E. A., McCombe, K. D., Craig, S. G., James, J. A., Brooks, J. M., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00624-8 · Cited by 47 · see citing works

Incorporating imaging information from deep neural network layers into image guided radiation therapy (IGRT)

Zhao, W., Han, B., Yang, Y., Buyyounouski, M. K., Hancock, S., Bagshaw, H. P., & Xing, L. (2019) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2019.06.027 · Cited by 46 · see citing works

Beyond Visual Interpretation: Quantitative Analysis and Artificial Intelligence in Interstitial Lung Disease Diagnosis “Expanding Horizons in Radiology”

Rea, G., Sverzellati, N., Bocchino, M., Lieto, R., Milanese, G., D’Alto, M., Bocchini, G., Maniscalco, M., Valente, T., & Sica, G. (2023) · Diagnostics · doi:10.3390/diagnostics13142333 · Cited by 43 · see citing works

The Role of the FDA in Ensuring the Safety and Efficacy of Artificial Intelligence Software and Devices

Allen, B. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2018.09.007 · Cited by 42 · see citing works

Survival Prediction of Brain Cancer with Incomplete Radiology, Pathology, Genomic, and Demographic Data

Cui, C., Liu, H., Liu, Q., Deng, R., Asad, Z., Wang, Y., Zhao, S., Yang, H., Landman, B. A., & Huo, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16443-9_60 · Cited by 42 · see citing works

Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction

Wang, Z., Lin, R., Li, Y., Zeng, J., Chen, Y., Ouyang, W., Li, H., Jia, X., Lai, Z., Yu, Y., et al. (2024) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbae012 · Cited by 40 · see citing works

A paradigm shift in point-of-care imaging in low-income and middle-income countries

Frija, G. G., Salama, D. H., Kawooya, M. G., & Allen, B. (2023) · EClinicalMedicine · doi:10.1016/j.eclinm.2023.102114 · Cited by 39 · see citing works

PathAL: An Active Learning Framework for Histopathology Image Analysis

Li, W., Li, J., Wang, Z., Polson, J., Sisk, A. E. J., Sajed, D. P., Speier, W. F., & Arnold, C. W. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3135002 · Cited by 38 · see citing works

Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation

Ho, D. J., Chui, M. H., Vanderbilt, C., Jung, J., Robson, M. E., Park, C.-S., Roh, J., & Fuchs, T. J. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100160 · Cited by 37 · see citing works

PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning

Aswolinskiy, W., Munari, E., Horlings, H. M., Mulder, L., Bogina, G. S., Sanders, J., Liu, Y.-H., van den Belt‐Dusebout, A. W., Tessier, L., Balkenhol, M. C. A., et al. (2023) · Breast Cancer Research · doi:10.1186/s13058-023-01726-0 · Cited by 37 · see citing works

A Framework for Interpretability in Machine Learning for Medical Imaging

Wang, A. Q., Karaman, B. K., Kim, H., Rosenthal, J., Saluja, R., Young, S. I., & Sabuncu, M. R. (2024) · IEEE Access · doi:10.1109/access.2024.3387702 · Cited by 37 · see citing works

Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment

Ho, D. J., Agaram, N. P., Schüffler, P. J., Vanderbilt, C., Jean, M.-H., Hameed, M. R., & Fuchs, T. J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59722-1_52 · Cited by 37 · see citing works

Deep Learning–Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction

Ho, D. J., Agaram, N. P., Jean, M.-H., Suser, S., Chu, C., Vanderbilt, C., Meyers, P. A., Wexler, L. H., Healey, J. H., Fuchs, T. J., et al. (2022) · American Journal Of Pathology · doi:10.1016/j.ajpath.2022.12.004 · Cited by 30 · see citing works

A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study

Lu, Y., Aslani, S., Zhao, A., Shahin, A. H., Barber, D., Emberton, M., Alexander, D. C., & Jacob, J. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e18695 · Cited by 30 · see citing works

AI‐Enhanced Detection of Clinically Relevant Structural and Functional Anomalies in MRI : Traversing the Landscape of Conventional to Explainable Approaches

Khosravi, P., Mohammadi, S., Zahiri, F., Khodarahmi, M., & Zahiri, J. (2024) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29247 · Cited by 30 · see citing works

Deep learning supported mitoses counting on whole slide images: A pilot study for validating breast cancer grading in the clinical workflow

van Bergeijk, S. A., Stathonikos, N., ter Hoeve, N. D., Lafarge, M. W., Nguyen, T. Q., van Diest, P. J., & Veta, M. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100316 · Cited by 29 · see citing works

Towards integration of artificial intelligence into medical devices as a real-time recommender system for personalised healthcare: State-of-the-art and future prospects

Iqbal, T., Masud, M., Amin, B., Feely, C., Faherty, M., Jones, T., Tierney, M. S., Shahzad, A., & Vazquez, P. (2024) · Health Sciences Review · doi:10.1016/j.hsr.2024.100150 · Cited by 28 · see citing works

Artificial intelligence-supported applications in head and neck cancer radiotherapy treatment planning and dose optimisation

Ahervo, H., Korhonen, J., Ming, S. L. W., Guan, F. Y., Soini, M., Lian, C. P. L., & Metsälä, E. (2023) · Radiography · doi:10.1016/j.radi.2023.02.018 · Cited by 24 · see citing works

DrasCLR: A self-supervised framework of learning disease-related and anatomy-specific representation for 3D lung CT images

Yu, K., Sun, L., Chen, J., Reynolds, M. J., Chaudhary, T., & Batmanghelich, K. N. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103062 · Cited by 24 · see citing works

Standardized Classification of Lung Adenocarcinoma Subtypes and Improvement of Grading Assessment Through Deep Learning

Lami, K., Ota, N., Yamaoka, S., Bychkov, A., Matsumoto, K., Uegami, W., Munkhdelger, J., Seki, K., Sukhbaatar, O., Attanoos, R. L., et al. (2023) · American Journal Of Pathology · doi:10.1016/j.ajpath.2023.07.002 · Cited by 23 · see citing works

AI-enhanced Mammography With Digital Breast Tomosynthesis for Breast Cancer Detection: Clinical Value and Comparison With Human Performance

Resch, D., Gullo, R. L., Teuwen, J., Semturs, F., Hummel, J. B., Resch, A., & Pinker, K. (2024) · Radiology Imaging Cancer · doi:10.1148/rycan.230149 · Cited by 23 · see citing works

Case Studies for Overcoming Challenges in Using Big Data in Cancer

Sweeney, S. M., Hamadeh, H. K., Abrams, N., Adam, S. J., Brenner, S., Connors, D. E., Davis, G. J., Fiore, L. D., Gawel, S. H., Grossman, R. L., et al. (2023) · Cancer Research · doi:10.1158/0008-5472.can-22-1277 · Cited by 21 · see citing works

MyopiaDETR: End-to-end pathological myopia detection based on transformer using 2D fundus images

Li, M., Liu, S., Wang, Z., Li, X. S., Yan, Z., Zhu, R., & Wan, Z. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1130609 · Cited by 19 · see citing works

Artificial intelligence in neuroradiology: a scoping review of some ethical challenges

Khosravi, P., & Schweitzer, M. (2023) · Frontiers in Radiology · doi:10.3389/fradi.2023.1149461 · Cited by 18 · see citing works

Development of AI-driven prediction models to realize real-time tumor tracking during radiotherapy

Zhou, D., Nakamura, M., Mukumoto, N., Tanabe, H., Iizuka, Y., Yoshimura, M., Kokubo, M., Matsuo, Y., & Mizowaki, T. (2022) · Radiation Oncology · doi:10.1186/s13014-022-02012-7 · Cited by 17 · see citing works

Pathology Issues Related to SN Procedures and Increased Detection of Micrometastases and Isolated Tumor Cells

van Diest, P. J., van Deurzen, C. H. M., & Cserni, G. (2010) · Breast Disease · doi:10.3233/bd-2010-0298 · Cited by 16 · see citing works

Inter and intra-observer reliability in histologic evaluation of necrosis rate induced by neo-adjuvant chemotherapy for osteosarcoma

Kang, J. W., Shin, S. H., Choi, J. H., Moon, K. C., Koh, J. S., Jung, C. K., Park, Y. K., Lee, K. B., & Chung, Y. G. (2017) · AJOU Open Repository (Ajou University) · Cited by 16 · see citing works

HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images

van Rijthoven, M., Balkenhol, M., Siliņa, K., van der Laak, J., & Ciompi, F. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101890 · Cited by 16 · see citing works

Artificial intelligence for radiomics; diagnostic biomarkers for neuro-oncology

Vahedifard, F., Hassani, S., Afrasiabi, A., & Esfe, A. M. (2022) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2022.14.3.0544 · Cited by 16 · see citing works

Choosing the right artificial intelligence solutions for your radiology department: key factors to consider

Alis, D. C., Tanyel, T., Meltem, E., Şeker, M. E., Şeker, D., Karakaş, H. M., Karaarslan, E., & Öksüz, İ. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.232658 · Cited by 16 · see citing works

Artificial intelligence for treatment delivery: image-guided radiotherapy

Rabe, M., Kurz, C., Thummerer, A., & Landry, G. (2024) · Strahlentherapie und Onkologie · doi:10.1007/s00066-024-02277-9 · Cited by 16 · see citing works

Hybrid QUS Radiomics: A Multimodal-Integrated Quantitative Ultrasound Radiomics for Assessing Ambulatory Function in Duchenne Muscular Dystrophy

Yan, D., Li, Q., Lin, C.-W., Shieh, J., Weng, W., & Tsui, P. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3330578 · Cited by 15 · see citing works

Abnormalities detection on chest radiograph with bounding box-based lungs extraction and object detection algorithm

Nguyễn, H. T., Nguyen, M. N., Pham, S. C., & Bui, P. H. D. (2024) · International Journal of Information Technology · doi:10.1007/s41870-023-01687-9 · Cited by 13 · see citing works

Avoiding Shortcut-Learning by Mutual Information Minimization in Deep Learning-Based Image Processing

Fay, L., Cobos, E., Yang, B., Gatidis, S., & Küstner, T. (2023) · IEEE Access · doi:10.1109/access.2023.3289397 · Cited by 12 · see citing works

PatchSorter: a high throughput deep learning digital pathology tool for object labeling

Walker, C., Talawalla, T., Tóth, R., Ambekar, A., Rea, K., Chamian, O., Fan, F., Berezowska, S., Rottenberg, S., Madabhushi, A., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01150-4 · Cited by 11 · see citing works

Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment

Wang, Y., Tadimalla, S., Hayden, A. J., Holloway, L. C., & Haworth, A. (2021) · Journal of Medical Imaging and Radiation Oncology · doi:10.1111/1754-9485.13242 · Cited by 10 · see citing works

A fusion model integrating magnetic resonance imaging radiomics and deep learning features for predicting alpha-thalassemia X-linked intellectual disability mutation status in isocitrate dehydrogenase–mutant high-grade astrocytoma: a multicenter study

Liu, Z., Xu, X.-Y., Zhang, W., Zhang, L.-Q., Wen, M., Gao, J., Yang, J., Kan, Y., Yang, X., Wen, Z.-F., et al. (2024) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-807 · Cited by 10 · see citing works

Challenges of artificial intelligence in precision oncology: public-private partnerships including national health agencies as an asset to make it happen.

Luu, V. P., Fiorini, M., Combes, S., Quemeneur, E., Bonneville, M., & Bousquet, P. (2023) · Annals of Oncology · doi:10.1016/j.annonc.2023.09.3106 · Cited by 9 · see citing works

Multi-instance learning based on spatial continuous category representation for case-level meningioma grading in MRI images

Li, J., Zhang, L., Shu, X., Teng, Y., & Xu, J. (2022) · Applied Intelligence · doi:10.1007/s10489-022-04114-x · Cited by 7 · see citing works

Evaluation of an AI-powered Portable Thermal Imaging Solution as a Pre-screening Tool for Breast Cancer

Berberian, N., Sargsyan, H., Sahakyan, L., Ghabuzyan, A. G., Geokchyan-Kzhdryan, H., Abnoosi, S., Leonian, A., Eriksson, K. D., & Gräwingholt, A. (2024) · Cancer Screening and Prevention · doi:10.14218/csp.2023.00034s · Cited by 7 · see citing works

AutoRadAI: a versatile artificial intelligence framework validated for detecting extracapsular extension in prostate cancer

Khosravi, P., Saikali, S., Alipour, A., Mohammadi, S., Boger, M., Diallo, D., Smith, C. J., Moschovas, M. C., Hajirasouliha, I., Hung, A. J., et al. (2025) · Biology Methods and Protocols · doi:10.1093/biomethods/bpaf032 · Cited by 6 · see citing works

Quantum Computing in Healthcare Using AI-Driven Medical Technologies

Anbazhagu, U. V., Priyadharshini, K., Deepajothi, S., Haripriya, M. P., & Banu, E. A. (2024) · Advances in computer and electrical engineering book series · doi:10.4018/979-8-3693-6875-6.ch014 · Cited by 6 · see citing works

DEPICTER: Deep representation clustering for histology annotation

Chelebian, E., Avenel, C., Ciompi, F., & Wählby, C. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108026 · Cited by 5 · see citing works

Future implications of artificial intelligence in lung cancer screening: a systematic review

Quirk, J., Donnchadha, C. M., Vaantaja, J., Mitchell, C., Marchi, N., AlSaleh, J., & Dalton, B. (2023) · BJR|Open · doi:10.1093/bjro/tzae035 · Cited by 5 · see citing works

Patient safety and healthcare quality of U.S. laboratory developed tests (LDTs) in the AI/ML era of precision medicine

Kurnat‐Thoma, E. L. (2024) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2024.1407513 · Cited by 4 · see citing works

Changing the workflow – Artificial intelligence in radiologic sciences

Jackson, A. J., & Hirsch, B. (2024) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2024.101710 · Cited by 1 · see citing works

Point of care molecular cancer diagnostics

Bhaskar, S., Umrao, S., Lee, H., Tibbs, J., Bacon, A. K., Shepherd, S., Ayupova, T., Ciloglu, F. U., Liu, L., Tan, A., et al. (2026) · Lab on a Chip · doi:10.1039/d5lc01014d · Cited by 17 · see citing works

Cervical Cancer Screening in the HPV-Vaccinated and Digital Era: Reassessing Strategies in Light of Artificial Intelligence and Evolving Risk

Galani, A., Zikopoulos, A., Moustakli, E., Potiris, A., Paraskevaidi, M., Arkoulis, I., Machairoudias, P., Stavrakaki, S. M., Kyrgiou, M., & Stavros, S. (2025) · Cancers · doi:10.3390/cancers17193179 · Cited by 14 · see citing works

Oral Cancer Diagnosis Using Histopathology Images: An Explainable Hybrid Transformer Framework

Cruze, F. R. D., Wasima, J., Hosen, M. F., Miah, M. B. A., Muhammad, Z., & Masud, F. A. (2026) · Technologies · doi:10.3390/technologies14010039 · Cited by 9 · see citing works

Artificial intelligence in genitourinary pathology

Patel, A., Parwani, A. V., & Satturwar, S. P. (2025) · Histopathology · doi:10.1111/his.70020 · Cited by 9 · see citing works

AI-Driven Advances in Precision Oncology: Toward Optimizing Cancer Diagnostics and Personalized Treatment

Bulić, L., Brlek, P., Hrvatin, N., Brenner, E., Škaro, V., Projić, P., Rogan, S. A., Bebek, M., Shah, P. S., & Primorac, D. (2026) · AI · doi:10.3390/ai7010011 · Cited by 7 · see citing works

Artificial intelligence in clinical oncology: Multimodal integration and translational development

Lin, R., Zhao, Z., Liu, Z., Kang, J., Zhang, K., Huang, X., & Yu, Y. (2026) · Cancer Letters · doi:10.1016/j.canlet.2026.218493 · Cited by 7 · see citing works

Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery

Kuno, M., Osumi, H., Udagawa, S., Yoshikawa, K., Ooki, A., Shinozaki, E., Ishikawa, T., Oba, J., Yamaguchi, K., & Sakurada, K. (2025) · Current Oncology · doi:10.3390/curroncol32110588 · Cited by 7 · see citing works

Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review)

Rizi, F. R., Jamadi, M. S., Rahimi, S., Bighamian, M., Paidar, P., Taki, M. J., & Bahmaie, N. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1763359 · Cited by 7 · see citing works

Explainable artificial intelligence for multi-modal cancer analysis: from genomics to immunology

Zhang, M., & Yin, L. (2026) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2025.105040 · Cited by 5 · see citing works

AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue

Sakaguchi, M., Yoshizawa, A., Masui, K., Sakai, T., & Komori, T. (2025) · Cancers · doi:10.3390/cancers18010009 · Cited by 6 · see citing works

A deep learning-based automated pipeline for colorectal cancer detection in contrast-enhanced CT images

Qiu, C., Miller, S., Subramanian, B., Ryu, A., Zhang, H., Fisher, G. A., Shah, N. H., Mongan, J., Langlotz, C. P., Poullos, P. D., et al. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102717 · Cited by 4 · see citing works

A quantitative study of pathologists’ perceptions towards artificial intelligence-assisted diagnostic system

Ye, Z., Qu, L., Wang, J., Jiang, Y., & Xue, P. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0001052 · Cited by 5 · see citing works

Diagnostic performance of convolutional neural network-based AI in detecting oral squamous cell carcinoma: a systematic review and meta-analysis

Shen, M., Jiang, Z., Feng, Y., Lin, Z., Lu, C., Sun, J., Yao, J., Hu, L., & Guo, J. (2026) · BMC Oral Health · doi:10.1186/s12903-025-07543-5 · Cited by 3 · see citing works

A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications

Liu, N., Han, G., Gu, Q., Zhang, Y., & Chen, M. M. (2026) · Cell Death and Disease · doi:10.1038/s41419-026-08769-z · Cited by 3 · see citing works

Artificial intelligence based quantification of T lymphocyte infiltrate predicts prognosis in high grade breast cancer using deep learning and statistical validation

Albalawi, E. S., Qayyum, J., & Qayyum, J. (2025) · Discover Oncology · doi:10.1007/s12672-025-04185-5 · Cited by 2 · see citing works

Going beyond genomics in precision oncology

Paassen, I., & Rubin, M. A. (2026) · Nature Medicine · doi:10.1038/s41591-025-04153-0 · Cited by 2 · see citing works

A Hybrid Quantum-Enhanced Sandwich Convolutional Neural Network for Medical Image Classification

Long, C., Li, S., Huang, M., Ye, X., & Sakurai, T. (2026) · Current Bioinformatics · doi:10.2174/0115748936418250251104114256 · Cited by 2 · see citing works

Reliable Radiologic Skeletal Muscle Area Assessment—A Biomarker for Cancer Cachexia Diagnosis

Ahmed, S., Parker, N. H., Park, M., Jeong, D. K., Peres, L. C., Davis, E. W., Permuth, J. B., Siegel, E. M., Schabath, M. B., Yilmaz, Y., et al. (2026) · Cells · doi:10.3390/cells15060515 · Cited by 2 · see citing works

Interdisciplinary integration and development trends of intelligent diagnosis in traditional Chinese medicine: a topic evolution analysis

Chenggong, X., Keying, H., Zhengquan, D., Xinyi, H., & Bin, W. (2026) · Digital Chinese Medicine · doi:10.1016/j.dcmed.2026.02.001 · Cited by 2 · see citing works

Outsmarting Metastatic Prostate Cancer: Integration of Imaging, Liquid Biopsies and Biomarkers With Artificial Intelligence

He, J., Li, L., Chen, S., Chen, R.-J., Zhuang, J.-L., Liu, C., Burrow, W., Chang, J., Tanziela, T., Bonfil, R. D., et al. (2026) · Technology in Cancer Research & Treatment · doi:10.1177/15330338261440434 · Cited by 2 · see citing works

Exploratory analysis of tumor budding density in resected synchronous and metachronous colorectal liver metastases

Petrović, N., Kukic, B. P., Šolajić, N., Maletin, N., Kolarov-Bjelobrk, I., Radić, J., Đermanović, A., & Nikolić, I. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1901885 · Cited by 2 · see citing works

Artificial intelligence in MRI clinical practice: From historical innovation to emerging trends

Aldahery, S. T. (2026) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2026.102158 · Cited by 1 · see citing works

Interpretable Knowledge Distillation Framework for Detection of Ewing Sarcoma in Young Adults

Harish, S., Manasvi, B. S., Gowda, M., & Bhargavi, K. (2025) · IEEE International Conference on Innovations in Engineering and Next-Generation Technologies for Sustainability (ICINVENTS) · doi:10.1109/icinvents64613.2025.11402162 · Cited by 1 · see citing works

Perineural invasion in solid tumors: biological foundations and the emerging integration of machine learning and artificial intelligence

Bahmad, H. F., Mirzabeigi, Y., Elajami, Y. K., Abou-Kheir, W. G., & Nair, N. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1801005 · Cited by 1 · see citing works

Schwann cells drive tumor progression through neuro-tumor crosstalk

Yang, L., Zheng, Y.-X., Liu, J., Xu, Y., Zhu, F.-Q., Zhang, W.-J., & Zou, R. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000003721

Multimodal Transformer Framework for Glioma Grading: Integrating MRI and Genomic Biomarkers with Explainable AI

Linija, K. P., & S.Rajesh (2025) · International Conference on Sustainable Communication Networks and Application (ICSCNA) · doi:10.1109/icscn67106.2025.11308700

Explainable Multi-Modal AI for Brain Tumor Segmentation and Radiotherapy Planning

Lanjewar, C. K., Kumar, L. S., Selvam, P., & Tiwari, A. K. (2026) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi68868.2026.11465232

Classification of serum protein and immunofixation electrophoresis images by computer vision-based deep learning models: A single- and dual-modality study

Alagoz, M. H., Erdem, S., Doğan, P., Derman, İ. U., Günver, M. G., Madenci, Ö. Ç., & Ademoğlu, E. (2026) · Annals of Clinical Biochemistry International Journal of Laboratory Medicine · doi:10.1177/00045632261454299

Enabling earlier detection of spinal lesions in CT imaging with artificial intelligence—a case study

Fritzsche, M., Kara-Schmidt, P., Kirchler, M., Schroeder, K., Wiedemeyer, C., & Braunschneider, L. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1767814

A weakly supervised deep learning-based recurrence prediction and risk stratification of lung adenocarcinoma from pathology whole-slide images

Xue, B., Liu, Y., Cai, M., Gu, L., Bai, J., Yao, L., Li, J., Wang, L., Huang, X. J., & Yao, D. (2026) · BMC Cancer · doi:10.1186/s12885-026-16340-4

Multimodal AI for Automated Staging of Neuroblastoma and Paediatric Sarcomas Using MRI, CT, and Reports

Zaytseva, V. I., Krivun, D. V., Ermolov, R. V., Pronina, A. R., Skripko, E. R., Myshak, V. L., Gichchieva, Z. A., Pavlenko, E. N., Kumratova, A. M., & Khastsaeva, L. R. (2026) · Journal Of Biochemical Technology · doi:10.51847/yye6e6ze9z

Looking Forward: A Special Issue of the Journal of Technology in Behavioral Science Focused on Suicide and Suicide Prevention

Pruitt, L. D., Armstrong, C. M., Smolenski, D. J., & Kohen, C. B. (2026) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-026-00679-3

AI-driven multimodal molecular imaging for precision oncology in breast cancer: Advances, challenges, and future directions

Xu, Z., Xie, Y., Li, Z., Li, K., Ding, Y., Chen, C., & Li, Z. (2026) · Molecular and Digital Medicine · doi:10.1016/j.mdmed.2026.100011

Assessment of prognostic stratification in colorectal cancer liver metastases using preoperative CT habitat imaging

Feng, H., Xue, M., He, Y., Zhang, G., & Xing, L. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1829205

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