DeepLesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning

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

Very Deep Convolutional Networks for Large-Scale Image Recognition

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ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

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 73,472

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

Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566

Microsoft COCO: Common Objects in Context

Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460

New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)

Eisenhauer, E. A., Therasse, P., Bogaerts, J., Schwartz, L. H., Sargent, D., Ford, R., Dancey, J., Arbuck, S., Gwyther, S., Mooney, M., et al. (2009) · European Journal of Cancer · doi:10.1016/j.ejca.2008.10.026 · Cited by 29,534

A survey on deep learning in medical image analysis

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332

An End-to-End Framework For Universal Lesion Detection With Missing Annotations

Bai, X., & Xia, Y. (2022) · International Conference on Signal Processing · doi:10.1109/icsp56322.2022.9965335 · Cited by 5

Development and Application of a Standardized Testset for an Artificial Intelligence Medical Device Intended for the Computer‐Aided Diagnosis of Diabetic Retinopathy

Wang, H., Meng, X., Tang, Q., Ye, H., Luo, Y., & Li, J. (2023) · Journal of Healthcare Engineering · doi:10.1155/2023/7139560 · Cited by 5

Few-View CT Image Reconstruction via Least-Squares Methods: Assessment and Optimization

Chillarón, M., Vidal, V., Verdú, G., & Quintana‐Ortí, G. (2023) · Nuclear Science and Engineering · doi:10.1080/00295639.2023.2199677 · Cited by 5

Deep Learning on Medical Imaging in Identifying Kidney Stones: Review Paper

Sulaksono, N., Adi, K., & Isnanto, D. R. (2023) · E3S Web of Conferences · doi:10.1051/e3sconf/202344802019 · Cited by 5

Enhancing placental pathology detection with GAMatrix-YOLOv8 model

Wu, W., Jiang, Z., Liu, J., Ji, J., Wei, X., Ye, X., & Zhang, Z. (2025) · Heliyon · doi:10.1016/j.heliyon.2025.e42441 · Cited by 6

Spinal Neoplasm Image Inpainting with Deep Convolutional Neutral Networks

Miao, Y., Sun, Y., Li, S., Zhang, P., Yang, Y., & Hu, Y. (2019) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio49542.2019.8961840 · Cited by 5

Attention-Embedded Decomposed Network with Unpaired CT Images Prior for Metal Artifact Reduction

Zhao, B., Ren, Q., Li, J., & Zhao, Y. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9413578 · Cited by 5

A Comprehensive Review on the Significance and Impact of Deep Learning in Medical Image Analysis

Panda, S., Jangid, M., & Jain, A. (2021) · International Conference on Technological Advancements and Innovations (ICTAI) · doi:10.1109/ictai53825.2021.9673189 · Cited by 5

Towards Sustainable Smart City via Resilient Internet of Things

Chui, K. T., de Pablos, P. O., Shen, C.-W., Lytras, M. D., & Vasant, P. (2022) · Contributions to management science · doi:10.1007/978-3-030-85954-1_8 · Cited by 5

Improving the Generalization of Deep Learning Classification Models in Medical Imaging Using Transfer Learning and Generative Adversarial Networks

Venu, S. K. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-10161-8_12 · Cited by 5

Universal Lesion Detection and Classification Using Limited Data and Weakly-Supervised Self-training

Naga, V., Mathai, T. S., Paul, A., & Summers, R. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16760-7_6 · Cited by 5

Idiosyncratic biases in the perception of medical images

Wang, Z., Manassi, M., Ren, Z., Ghirardo, C., Cañas-Bajo, T., Murai, Y., Zhou, M., & Whitney, D. C. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1049831 · Cited by 4

Vision Transformer with Progressive Tokenization for CT Metal Artifact Reduction

Zheng, S., Zhang, D., Yu, C., Zhu, D., Zhu, L., Liu, H., & Huang, Z. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10095579 · Cited by 5

Meta semi-supervised medical image segmentation with label hierarchy

Xu, H. Y., Xie, H., Tan, Q., & Zhang, Y. (2023) · Health Information Science and Systems · doi:10.1007/s13755-023-00222-1 · Cited by 5

Investigating the Effects of Transfer Learning on ROI-based Classification of Chest CT Images: A Case Study on Diffuse Lung Diseases

Mabu, S., Atsumo, A., Kido, S., Kuremoto, T., & Hirano, Y. (2020) · Journal of Signal Processing Systems · doi:10.1007/s11265-019-01499-w · Cited by 5

The double-edged sword: A critical review of foundational medical datasets for AI benchmarks, biases, and the future of equitable healthcare

Ramadan, R. Α., Madi, N. K. M., Fageeri, S. O., Yousif, K. A., Al-Daweri, M. S., & Rajhi, W. K. H. A. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.132919 · Cited by 5

A comprehensive review of CT artifact reduction: From traditional reduction techniques to deep learning methods

Chen, K., Wang, Y., Ji, S., Meng, C., Pan, C., Tang, Z., & Chen, D. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102728 · Cited by 4

Combating deep fakes by the power of artificial intelligence and block chain in healthcare applications

Chandrappa, K., & Shankar, K. K. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-99481-1.00012-2 · Cited by 4

Graph-Theoretic Automatic Lesion Tracking and Detection of Patterns of Lesion Changes in Longitudinal CT Studies

Veroli, B. D., Lederman, R., Sosna, J., & Leo Joskowicz, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43904-9_11 · Cited by 4

3D Segmentation of Necrotic Lung Lesions in CT Images Using Self-Supervised Contrastive Learning

Liu, Y., Halek, S., Crawford, R., Persson, K., Tomaszewski, M. R., Wang, S., Baumgartner, R., Yuan, J. D., Goldmacher, G., & Chen, A. (2024) · IEEE Access · doi:10.1109/access.2024.3363637 · Cited by 4

Weakly-supervised detection of bone lesions in CT

Sheng, T., Mathai, T. S., Shieh, A., & Summers, R. M. (2024) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.3008823 · Cited by 4

Pre-Trained Transformer-Based Parallel Multi-Channel Adaptive Image Sequence Interpolation Network

Liu, H., Chen, G., Liu, M., & Nie, L. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3409395 · Cited by 6

Curriculum meta-learning for zero-shot cross-lingual transfer

Doan, T., & Le, B. H. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112238 · Cited by 4

Robust localization of poorly visible tumor in fiducial free stereotactic body radiation therapy

Xu, D., Descovich, M., Liu, H., & Sheng, K. (2024) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2024.110514 · Cited by 4

Cascaded Frequency-Encoded Multi-Scale Neural Fields for Sparse-View CT Reconstruction

Wu, J., Lin, J., Pang, Y., Jiang, X., Li, X., Meng, H., Luo, Y., Yang, L., & Li, Z. (2025) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2025.3536078 · Cited by 4

Fast Lung Localization in Computed Tomography by a 1D Detection Network

Proskurov, V., Kurmukov, A. G., Pisov, M., & Belyaev, M. (2021) · Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) · doi:10.1109/usbereit51232.2021.9455083 · Cited by 4

Window Loss for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation

Zhang, X., Wang, Y., Cheng, C., Lü, L., Harrison, A. P., Xiao, J., Liao, C., & Miao, S. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16153 · Cited by 4

Hetero-Modal Learning and Expansive Consistency Constraints for Semi-supervised Detection from Multi-sequence Data

Lai, B., Wu, Y.-H., Zhou, X.-Y., Wang, P., Lü, L., Huang, L., Han, M., Xiao, J. Z., Hu, H., & Harrison, A. P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87589-3_31 · Cited by 4

Faster R-CNN based on Optimized SqueezeNet for Liver Lesion Detection from DeepLesion Dataset

Alkhaleefah, M., Tan, T.-H., Achhannagari, V. P., Ma, S., Tsai, M.-J., & Chang, Y.-L. (2021) · International Conference on Graphics and Signal Processing (ICGSP) · doi:10.1145/3474906.3474914 · Cited by 4

Size‐adaptive mediastinal multilesion detection in chest CT images via deep learning and a benchmark dataset

Wang, J., Ji, X., Zhao, M., Wen, Y., She, Y., Deng, J., Chen, C., Qian, D., Lu, H., & Zhao, D. (2022) · Medical Physics · doi:10.1002/mp.15804 · Cited by 4

Optimal Bilateral Filtering of CT Images

Draganov, I. R., & Gаnchеvа, V. (2021) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci54926.2021.00053 · Cited by 4

Direct image reconstruction from raw measurement data using an encoding transform refinement-and-scaling neural network

Whiteley, W., & Gregor, J. (2019) · International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D) · doi:10.1117/12.2534907 · Cited by 4

Deep Ensemble Learning Network for Kidney Lesion Detection

Xiong, Z., Zhang, H., Chen, Y., & Song, Y. (2019) · Chinese Automation Congress · doi:10.1109/cac48633.2019.8997272 · Cited by 4

Data-driven deep density estimation

Puchert, P., Hermosilla, P., Ritschel, T., & Ropinski, T. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06281-3 · Cited by 4

Computed tomography medical image reconstruction on affordable equipment by using Out-Of-Core techniques

Chillarón, M., Quintana‐Ortí, G., Vidal, V., & Verdú, G. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105488 · Cited by 3

Utilizing domain knowledge to improve the classification of intravenous contrast phase of CT scans

Liu, L., Liu, J., Santra, B., Parnell, C., Mukherjee, P., Mathai, T. S., Zhu, Y., Anand, A., & Summers, R. M. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102458 · Cited by 3

DROLL: Dual-Domain Reconstruction Network With a High-Fidelity Domain-Transform Operator Based on Learned Low-Rank Prior for Sparse-View CT Reconstruction

Zhang, H., Zhang, P., Zhang, Y., Chen, Y., Liu, Y., & Gui, Z. (2025) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2025.3617255 · Cited by 3

Differentiable Neural Architecture Search for medical image segmentation: A systematic review and field audit

Benedykciuk, E., Denkowski, M., & Wójcik, G. M. (2026) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2026.102713 · Cited by 4

Learning with less supervision: A survey of label-efficient learning for medical image analysis

Jin, C., Guo, Z., Lin, Y., Luo, L., & Chen, H. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104062 · Cited by 49

IAFI-FCOS: Intra- and across-layer feature interaction FCOS model for lesion detection of CT images

Guan, Q., Pan, M., Chen, F., Yang, Z., Yu, Z., Zhou, Q., & Hu, H. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650925 · Cited by 3

Performance Review of Meta LLaMa 3.1 in Thoracic Imaging and Diagnostics

Lotfian, G., Parekh, K., & Suthar, P. P. (2025) · iRadiology · doi:10.1002/ird3.70013 · Cited by 4

End-to-end prediction of clinical outcomes in head and neck squamous cell carcinoma with foundation model-based multiple instance learning

Meneghetti, A. R., Ligero, M., Kühn, J., Löck, S., Carrero, Z. I., Pérez-López, R., Bressem, K. K., Brinker, T. J., Pearson, A. T., Truhn, D., et al. (2025) · BMC Artificial Intelligence · doi:10.1186/s44398-025-00003-8 · Cited by 3

Cascaded multi-point regression Network for high-quality generic lesion detection

Li, H., Rong, H., Sheng, V. S., Song, Y., Qiu, C., Han, K., & Liu, Z. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119141 · Cited by 3

Automated Classification of Urinary Cells: Using Convolutional Neural Network Pre-trained on Lung Cells

Teramoto, A., Michiba, A., Kiriyama, Y., Sakurai, E., Shiroki, R., & Tsukamoto, T. (2023) · Applied Sciences · doi:10.3390/app13031763 · Cited by 3

Intraoperative CT Augmentation for Needle-Based Liver Interventions

Hadramy, S. E., Verde, J. M., Padoy, N., & Cotin, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43996-4_28 · Cited by 3

Confidence-Aware and Self-supervised Image Anomaly Localisation

Müller, J. P., Baugh, M., Tan, J., Dombrowski, M., & Kainz, B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44336-7_18 · Cited by 4

Lesion Detection in Optical Coherence Tomography with Transformer-Enhanced Detector

Ahmed, H., Zhang, Q., Wong, F. S. L., Donnan, R. S., & Alomainy, A. (2023) · Journal of Imaging · doi:10.3390/jimaging9110244 · Cited by 3

Brain Tumour Detection and Segmentation using CNN Architecture and U-Net Architecture

Totad, S. R., Meeradevi, M., Sowmya, B. J., Boppudi, P., Yaswanth, N., & Nivedith, I. (2023) · International Conference on Integrated Intelligence and Communication Systems (ICIICS) · doi:10.1109/iciics59993.2023.10421540 · Cited by 3

Efficient Transferability Assessment for Selection of Pre-trained Detectors

Zhao, W., Li, A., Li, Z., & Dou, Q. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00170 · Cited by 4

A modified Tseng algorithm approach to restoring thoracic diseases’ computerized tomography images

Umar, H., Adamu, A., Hijaz, A., & Ozsahin, D. U. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305728 · Cited by 3

A quantitative analysis of the improvement provided by comprehensive annotation on CT lesion detection using deep learning

Ma, J., Yoon, J. H., Lin, L., Yang, H., Guo, P., Yang, D., Li, J., Shen, J., Schwartz, L. H., & Zhao, B. (2024) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.14434 · Cited by 3

DCDiff: Dual-Domain Conditional Diffusion for CT Metal Artifact Reduction

Shen, R., Li, X., Li, Y.-F., Sui, C., Yu, P., & Ke, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72104-5_22 · Cited by 3

Meply: A Large-scale Dataset and Baseline Evaluations for Metastatic Perirectal Lymph Node Detection and Segmentation

Guo, W., Zhang, H., Wan, S., Zou, B., Wang, W., Qiu, C., Li, J., & Jin, P. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8496-7_25 · Cited by 3

MAReraser: Metal Artifact Reduction with Image Prior Using CNN and Transformer Together

Zheng, S., Zhang, D., Yu, C., Jia, L., Zhu, L., Huang, Z., Zhu, D., & Yu, H. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822394 · Cited by 3

Predicting population dynamics of antimicrobial resistance using mechanistic modeling and machine learning

Zhou, Z., Shyti, I., Kim, J., & You, L. (2025) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2025.115661 · Cited by 3

MediSee: Reasoning-Based Pixel-Level Perception in Medical Images

Tong, Q., Lu, Z., Liu, J., Zheng, Y., & Lu, Z. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3754736 · Cited by 10

Medical Image Enhancement For Lesion Detection Based On Class-Aware Attention And Deep Colorization

Guo, J., Chen, J., Lu, C., & Huang, H. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434019 · Cited by 3

Unsharp Masking with Local Adaptive Contrast Enhancement of Medical Images

Draganov, I. R., & Gаnchеvа, V. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-3880-0_37 · Cited by 3

A Set of Comprehensive Evaluation System for Different Data Augmentation Methods

Zhang, C., Zhang, X., & Tu, D. (2022) · Mobile Information Systems · doi:10.1155/2022/8572852 · Cited by 3

Delving into Universal Lesion Segmentation: Method, Dataset, and Benchmark

Qiu, Y., & Xu, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20074-8_28 · Cited by 3

Factoring 3D Convolutions for Medical Images by Depth-wise Dependencies-induced Adaptive Attention

Zeng, N., Fang, J., Wang, X., Lu, X., Huang, J., Miao, H., & Liu, J. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9995195 · Cited by 3

Fine-Grained Lesion Annotation in CT Images With Knowledge Mined From Radiology Reports

Yan, K., Peng, Y., Lu, Z., & Summers, R. M. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759336 · Cited by 3

3D SUSAN Filtering of CT Images With Adaptive Selection of the Brightness Threshold

Draganov, I. R., & Mironov, R. (2021) · International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST) · doi:10.1109/icest52640.2021.9483499 · Cited by 3

Weakly Supervised Multi-Organ Multi-Disease Classification of Body CT Scans.Preprint

Tushar, F. I., D’Anniballe, V. M., Hou, R., Mazurowski, M. A., Fu, W., Samei, E., Rubin, G. D., & Lo, J. Y.-C. (2020) · arXiv (Cornell University) · Cited by 3

Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask SupervisionPreprint

Wang, C., Xu, R., Xu, S., Meng, W., Xiao, J., & Zhang, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.14556 · Cited by 3

Deploying clinical machine learning? Consider the following...Preprint

Lu, C., Chang, K., Singh, P., Pomerantz, S. R., Doyle, S., Kakarmath, S. S., Bridge, C. P., & Kalpathy–Cramer, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.06919 · Cited by 3

Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning

Tushar, F. I., D’Anniballe, V. M., Hou, R., Mazurowski, M. A., Fu, W., Samei, E., Rubin, G. D., & Lo, J. Y.-C. (2021) · Radiology Artificial Intelligence · doi:10.1148/ryai.210026 · Cited by 29

FIND-Net – Fourier-Integrated Network with Dictionary Kernels for Metal Artifact Reduction

Tasharofi, F., Fan, F., Qahqaie, M., Thies, M., & Maier, A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05169-1_19 · Cited by 2

Enhancing Endoscopic Image Retrieval via Self-Supervised Learning and Large VLM-Based Re-ranking

Tran, K., Ly, L., Ho, D. K., & Luong, N. H. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3762082 · Cited by 2

Computer Vision and Deep Learning in Regenerative Medicine: Part 1

Wen, Y., & Ye, X. (2025) · Springer eBooks · doi:10.1007/978-3-031-77456-0_5 · Cited by 2

SuperAMUS-GAN: An image-domain based supervised adversarial multi-scale U-Net synthesis model for CT metal artefacts reduction

Tariq, B., Khan, O., Francis, N., Aung, Z., Maalej, N. M., Memisoglu, E. I., & Raja, A. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109903 · Cited by 2

Universal pre-training for generalizable incomplete-view CT reconstruction

Ma, C., Li, Z., He, J., Zhang, J., Zhang, Y., & Shan, H. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113513 · Cited by 2

Automated detection of lesion in computer tomography images based on Cascade R-CNN

Liu, R., Zhao, Y., Liu, Y., Chen, X., Cui, S., Wang, F., & Lin, Y. (2020) · International Conference on Image, Video Processing and Artificial Intelligence (IVPAI) · doi:10.1117/12.2579644 · Cited by 2

Automatic Detection of Extra-Cardiac Findings in Cardiovascular Magnetic Resonance

Wickremasinghe, D. H., Khenkina, N., Masci, P. G., King, A. P., & Anton, E. P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80432-9_8 · Cited by 2

Class Imbalance Correction for Improved Universal Lesion Detection and Tagging in CT

Erickson, P. D., Mathai, T. S., & Summers, R. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16760-7_17 · Cited by 2

Researches advanced in application of medical image analysis based on deep learning

Hu, X., Guo, Z., Yang, S., & Zheng, K. (2022) · International Conference on Computer, Artificial Intelligence and Control Engineering (CAICE) · doi:10.1117/12.2641098 · Cited by 2

An Innovative Metal Artifact Reduction Algorithm based on Res-U-Net GANs

Zhang, Z., Yang, M., Xu, L., Yang, J., Guo, H., & Wang, J. (2023) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405619666230217102534 · Cited by 2

Compact Image Transformer Based on Convolutional Variational Autoencoder with Augmented Attention Backbone for Target Recognition in Infrared Images

Nebili, B., Khellal, A., Nemra, A., Boulahia, S. Y., & Mascarilla, L. (2023) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-023-08012-3 · Cited by 2

Robustness Assessment of AI-Based 2D Object Detection Systems: A Method and Lessons Learned from Two Industrial Cases

Wozniak, A.-L., Segura, S., & Mazo, R. (2024) · Electronics · doi:10.3390/electronics13071368 · Cited by 4

Exploratory Training for Universal Lesion Detection: Enhancing Lesion Mining Quality Through Temporal Verification

Bai, X., Chen, G., Ma, B., Li, C., Zhang, J., & Xia, Y. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3417274 · Cited by 2

Comparative analysis of FISTA and inertial Tseng algorithm for enhanced image restoration in prostate cancer imaging

Adamu, A., Umar, H., Akinade, S. E., & Ozsahin, D. U. (2024) · Applied Mathematics in Science and Engineering · doi:10.1080/27690911.2024.2388247 · Cited by 2

Tuning Stable Rank Shrinkage: Aiming at the Overlooked Structural Risk in Fine-tuning

Shen, S., Zhou, Y., Wei, B., Chang, E., & Xu, Y. L. (2024) · 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/cvpr52733.2024.02690 · Cited by 2

Research on Plant Pest and Disease Classification Based on Self-Supervised Learning

Pei, M., Peng, W., & Pei, M. (2024) · Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC) · doi:10.1109/ipec61310.2024.00075 · Cited by 2

Depth-Aware Networks for Multi-Organ Lesion Detection in Chest CT Scans

Zhang, H., & Chung, A. C. S. (2024) · Bioengineering · doi:10.3390/bioengineering11100998 · Cited by 2

Development of a No-Reference CT Image Quality Assessment Method Using RadImageNet Pre-trained Deep Learning Models

Ohashi, K., Nagatani, Y., Yamazaki, A., Yoshigoe, M., Iwai, K., Uemura, R., Shimomura, M., Tanimura, K., & Ishida, T. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01542-2 · Cited by 2

Artificial intelligence in gastrointestinal radiology: A review with special focus on recent development of magnetic resonance and computed tomography

Chang, K.-P., Lin, S.-H., & Chu, Y.-W. (2021) · Artificial Intelligence in Gastroenterology · doi:10.35712/aig.v2.i2.27 · Cited by 2

Lesion Detection and Classification Using Sematic Deep Segmentation Network

Gavade, A. B., Nerli, R. B., & Ghagane, S. C. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-1773-7_26 · Cited by 2

Machine Learning and Deep Learning in Oncologic Imaging: Potential Hurdles, Opportunities for Improvement, and Solutions—Abdominal Imagers' Perspective

Yedururi, S., Morani, A. C., Katabathina, V. S., Jo, N., Rachamallu, M., Prasad, S. R., & Marcal, L. P. (2021) · Journal of Computer Assisted Tomography · doi:10.1097/rct.0000000000001183 · Cited by 2

Automated Assessment of Renal Calculi in Serial Computed Tomography Scans

Mukherjee, P., Lee, S., Pickhardt, P. J., & Summers, R. M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17721-7_5 · Cited by 2

Universal Lesion Detection Utilising Cascading R-CNNs and a Novel Video Pretraining Method

Amiriparian, S., Meiners, A., Rothenpieler, D., Kathan, A., Gerczuk, M., & Schuller, B. W. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340964 · Cited by 2

Lung Cancer Detection Using Image Processing

Varaprasad, R., Sharath, K. Y. H., Shamoil, A., Raj, G. S., & ., M. Z. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-9442-7_55 · Cited by 2

Accurate 2D soft segmentation of medical image via SoftGAN network.Preprint

Wang, C., Xu, R., Xu, S., Meng, W., Xiao, J., Peng, Q., & Zhang, X. (2020) · arXiv (Cornell University) · Cited by 2

SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous PerturbationPreprint

Zhou, H.-Y., Wang, C., Li, H., Wang, G., Zhang, S., Li, W., & Yu, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.01544 · Cited by 2

Federated Learning for Protecting Medical Data Privacy

Punreddy, A. R. (2023) · doi:10.31979/etd.cfgv-t6wa · Cited by 2

An overview of abdominal segmentation methods using General Adversarial Network (GAN)

LIU, Y. (2022) · International Conference on Biomedical and Intelligent Systems (IC-BIS) · doi:10.1117/12.2660662 · Cited by 1

ILKA-Net: CT Metal Artifact Reduction Network Based on Large Kernel Attention

Zhang, X., Jia, Y., Cui, J., & Ye, C. (2023) · International Conference on Intelligent Systems and Knowledge Engineering (ISKE) · doi:10.1109/iske60036.2023.10481165 · Cited by 1

Adipose Tissue Segmentation after Lung Slice Localization in Chest CT Images Based on ConvBiGRU and Multi-Module UNet

Lei, P., Li, J., Yi, J., & Chen, W. (2024) · Biomedicines · doi:10.3390/biomedicines12051061 · Cited by 1

Development and validation of a self-attention network-based algorithm to detect mediastinal lesions on computed tomography images

Wu, S., Liu, S., Zhong, M., de Loos, E. R., Hartert, M., Fuentes-Martín, Á., Lenzini, A., Wang, D., & Qing, Q. (2024) · Journal of Thoracic Disease · doi:10.21037/jtd-24-679 · Cited by 1

Weakly supervised pre-training for brain tumor segmentation using principal axis measurements of tumor burden

Mckone, J. E., Lambrou, T., Ye, X., & Brown, J. M. (2024) · Frontiers in Computer Science · doi:10.3389/fcomp.2024.1386514 · Cited by 1

KI-Technologieradar im Gesundheitswesen – Ein Überblick mit Praxisbeispielen

Thumm, D., & Pfannstiel, M. A. (2024) · Springer eBooks · doi:10.1007/978-3-658-43860-9_15 · Cited by 1

DeepLesion: Automated Deep Mining, Categorization and Detection of Significant Radiology Image Findings using Large-Scale Clinical Lesion Annotations

Yan, K., Wang, X., Lu, L., & Summers, R. M. (2017) · arXiv · doi:10.48550/arxiv.1710.01766 · Cited by 40

Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled DatasetsPreprint

Yan, K., Cai, J., Harrison, A. P., Jin, D., Xiao, J. Z., & Lü, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.13753 · Cited by 8

Lesion-Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale

Cai, J., Harrison, A. P., Zheng, Y., Yan, K., Huo, Y., Xiao, J., Yang, L., & Lu, L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3022034 · Cited by 34

Learning From Multiple Datasets With Heterogeneous and Partial Labels for Universal Lesion Detection in CT

Yan, K., Cai, J., Zheng, Y., Harrison, A. P., Jin, D., Tang, Y., Tang, Y., Huang, L., Xiao, J. Z., & Lü, L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3047598 · Cited by 92

An End-to-End Framework For Universal Lesion Detection With Missing Annotations

Bai, X., & Xia, Y. (2022) · International Conference on Signal Processing · doi:10.1109/icsp56322.2022.9965335 · Cited by 5