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

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

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

Classification performance bias between training and test sets in a limited mammography dataset

Hou, R., Lo, J. Y.-C., Marks, J. R., Hwang, E., & Grimm, L. J. (2024) · PLoS ONE · doi:10.1371/journal.pone.0282402 · Cited by 19

Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

Liao, H., Lin, W.-A., Yuan, J., Zhou, S. K., & Luo, J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32226-7_23 · Cited by 14

Fine-grained spatial information extraction in radiology as two-turn question answering

Datta, S., & Roberts, K. E. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104628 · Cited by 15

Diagnosis of Pulmonary Nodules on CT Images Using YOLOv4

Bhatt, S. D., Soni, H. B., Pawar, T., & Kher, H. R. (2022) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v18i05.29529 · Cited by 14

Weakly Supervised Lesion Co-Segmentation on Ct Scans

Agarwal, V., Tang, Y., Xiao, J. Z., & Summers, R. M. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098657 · Cited by 14

Recist-Net: Lesion Detection Via Grouping Keypoints On Recist-Based Annotation

Xie, C., Cao, S., Wei, D., Zhou, H.-Y., Ma, K., Zhang, X., Qian, B., Wang, L., & Zheng, Y. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433794 · Cited by 14

CPLOYO: A pulmonary nodule detection model with multi-scale feature fusion and nonlinear feature learning

Wang, M., Yang, Z. J., Zhao, R., & Jiang, Y. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2025.03.032 · Cited by 13

Semisupervised Training of a Brain MRI Tumor Detection Model Using Mined Annotations

Swinburne, N., Yadav, V. H., Kim, J., Choi, Y. R., Gutman, D. C., Yang, J. T., Moss, N. S., Stone, J. B., Tisnado, J., Hatzoglou, V., et al. (2022) · Radiology · doi:10.1148/radiol.210817 · Cited by 13

QN-Mixer: A Quasi-Newton MLP-Mixer Model for Sparse-View CT Reconstruction

Ayad, I., Larue, N., & Nguyen, M. K. (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/cvpr52733.2024.02392 · Cited by 13

One stage lesion detection based on 3D context convolutional neural networks

Cai, G., Chen, J., Wu, Z., Tang, H., Liu, Y., Wang, S., & Su, S. (2019) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2019.106449 · Cited by 13

Cervical cancer detection in cervical smear images using deep pyramid inference with refinement and spatial‐aware booster

Ma, D., Liu, J., Li, J., & Zhou, Y. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2020.0688 · Cited by 13

CT Data Curation for Liver Patients: Phase Recognition in Dynamic Contrast-Enhanced CT

Zhou, B., Harrison, A. P., Yao, J., Cheng, C., Xiao, J. Z., Liao, C., & Lü, L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33391-1_16 · Cited by 13

A Review of Generative Adversarial Networks in Cancer Imaging: New Applications, New Solutions.

Osuala, R. O., Kushibar, K., Garrucho, L., Linardos, A., Szafranowska, Z., Klein, S., Glocker, B., Díaz, O., & Lekadir, K. (2021) · Spiral (Imperial College London) · Cited by 13

Hallucinations in medical devices

Granstedt, J., Prabhat, K. C., Deshpande, R., Garcia, V. L., & Badano, A. G. (2025) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2025.100145 · Cited by 18

A despeckling method for ultrasound images utilizing content-aware prior and attention-driven techniques

Qiu, C., Huang, Z., Lin, C., Zhang, G., & Ying, S. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107515 · Cited by 12

Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling

Elata, N., Michaeli, T., & Elad, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73229-4_17 · Cited by 12

Identifying Parkinson’s Disease Through the Classification of Audio Recording Data

Bielby, J., Kühn, S., Colreavy‐Donnelly, S., Caraffini, F., O’Connor, S., & Anastassi, Z. A. (2020) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec48606.2020.9185915 · Cited by 12

Machine learning

SCHNEIDER, P. L., & Xhafa, F. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-823818-9.00019-5 · Cited by 17

Deep Active Learning for Effective Pulmonary Nodule Detection

Liu, J., Cao, L., & Tian, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59725-2_59 · Cited by 12

Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images.Preprint

Yan, K., Cai, J., Jin, D., Miao, S., Harrison, A. P., Guo, D., Tang, Y., Xiao, J. Z., Lu, J., & Lü, L. (2020) · arXiv (Cornell University) · Cited by 12

AAPM CT metal artifact reduction grand challenge

Haneda, E., Peters, N., Zhang, J., Karageorgos, G. M., Xia, W., Paganetti, H., Wang, G., Guo, Y., Ma, J., Park, H. S., et al. (2025) · Medical Physics · doi:10.1002/mp.70050 · Cited by 15

Limited-Angle CT Reconstruction with Generative Adversarial Network Sinogram Inpainting and Unsupervised Artifact Removal

Xie, E., Ni, P., Zhang, R., & Li, X. (2022) · Applied Sciences · doi:10.3390/app12126268 · Cited by 11

ST-Double-Net: A Two-Stage Breast Tumor Classification Model Based on Swin Transformer and Weakly Supervised Target Localization

Hao, S., Jia, Y. D., Liu, J., Wang, Z., Liu, C., Ji, Z., & Ганчев, И. (2024) · IEEE Access · doi:10.1109/access.2024.3445954 · Cited by 11

Breast Mass Detection With Faster R-CNN: On the Feasibility of Learning From Noisy Annotations

Famouri, S., Morra, L., Mangia, L., & Lamberti, F. (2021) · IEEE Access · doi:10.1109/access.2021.3072997 · Cited by 13

Super-Resolution Network with Information Distillation and Multi-Scale Attention for Medical CT Image

Zhao, T., Hu, L., Zhang, Y., & Fang, J. (2021) · Sensors · doi:10.3390/s21206870 · Cited by 11

Accurate and Robust Lesion RECIST Diameter Prediction and Segmentation with Transformers

Tang, Y., Zhang, N., Wang, Y., He, S., Han, M., Xiao, J. Z., & Lin, R.-S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16440-8_51 · Cited by 11

Mask R-CNN assisted 2.5D object detection pipeline of 68Ga-PSMA-11 PET/CT-positive metastatic pelvic lymph node after radical prostatectomy from solely CT imaging

Xu, D., Ma, M., Cao, M., Kishan, A. U., Nickols, N. G., Scalzo, F., & Sheng, K. (2023) · Scientific Reports · doi:10.1038/s41598-023-28669-y · Cited by 12

AI Model for Detection of Abdominal Hemorrhage Lesions in Abdominal CT Images

Park, Y., Cho, H., & Kim, M. (2023) · Bioengineering · doi:10.3390/bioengineering10040502 · Cited by 11

Graph-based automatic detection and classification of lesion changes in pairs of CT studies for oncology follow-up

Rochman, S., Szeskin, A., Lederman, R., Sosna, J., & Leo Joskowicz, B. (2023) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-023-03000-2 · Cited by 11

A deep learning model for automated kidney calculi detection on non-contrast computed tomography scans in dogs

Ji, Y., Hwang, G., Lee, S. J., Lee, K., & Yoon, H. (2023) · Frontiers in Veterinary Science · doi:10.3389/fvets.2023.1236579 · Cited by 14

Kidney Stone Detection from CT Images Using ALEXNET and Hybrid ALEXNET-RF Models

Revathi, M., & Raghuraman, G. (2023) · Journal of Circuits Systems and Computers · doi:10.1142/s021812662450107x · Cited by 11

Few‐shot learning with deformable convolution for multiscale lesion detection in mammography

Li, C., Zhang, D., Tian, Z., Du, S., & Qu, Y. (2020) · Medical Physics · doi:10.1002/mp.14129 · Cited by 10

Lesion Segmentation and RECIST Diameter Prediction via Click-Driven Attention and Dual-Path Connection

Tang, Y., Yan, K., Cai, J., Huang, L., Xie, G., Xiao, J. Z., Lu, J., Lin, G., & Lü, L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87196-3_32 · Cited by 11

Liver Cancer Detection Using Hybrid Approach-Based Convolutional Neural Network (HABCNN)

Bhushan, S. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-2828-4_23 · Cited by 10

Customized m-RCNN and hybrid deep classifier for liver cancer segmentation and classification

Khan, R., Su, L., Zaman, A., Hassan, H., Kang, Y., & Huang, B. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e30528 · Cited by 10

Effective Lymph Nodes Detection in CT Scans Using Location Debiased Query Selection and Contrastive Query Representation in Transformer

Yu, Q., Wang, Y., Yan, K., Li, H., Guo, D., Zhang, L., Shen, N., Wang, Q., Ding, X., Lü, L., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72946-1_11 · Cited by 12

A Comprehensive Study of Applying Object Detection Methods for Medical Image Analysis

Ganatra, N. (2021) · International Conference on Computing for Sustainable Global Development · doi:10.1109/indiacom51348.2021.00147 · Cited by 10

An Efficient Anchor-Free Universal Lesion Detection in Ct-Scans

Sheoran, M., Dani, M., Sharma, M., & Vig, L. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761698 · Cited by 10

Computer-Aided Tuberculosis Diagnosis with Attribute Reasoning Assistance

Pan, C., Zhao, G., Fang, J., Qi, B., Liu, J., Fang, C., Zhang, D., Li, J., & Yu, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16431-6_59 · Cited by 10

Artificial intelligence for early detection of renal cancer in computed tomography: A review

McGough, W. C., Sanchez, L. E., McCague, C., Stewart, G. D., Schönlieb, C., Sala, E., & Crispin‐Ortuzar, M. (2022) · Cambridge Prisms Precision Medicine · doi:10.1017/pcm.2022.9 · Cited by 11

Non-Invasive Assessment of Lung Water Content Using Chest Patch RF Sensors: A Computer Study Using NIH Patients CT Scan Database and AI Classification Algorithms

Leong, C., Xiao, Y., Yun, Z., & Iskander, M. F. (2023) · IEEE Access · doi:10.1109/access.2023.3238969 · Cited by 11

Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning

Wu, Z., Yang, Y., Gu, J., & Volker Tresp, C. (2021) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi52183.2021.00022 · Cited by 11

Universal lesion detection in CT scans using neural network ensembles

Mattikalli, T., Mathai, T. S., & Summers, R. M. (2022) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2612660 · Cited by 10

Improving CT-image universal lesion detection with comprehensive data and feature enhancements

Liu, Z., Han, K., Xue, K., Song, Y., Liu, L., Tang, Y., & Zhu, Y. (2022) · Multimedia Systems · doi:10.1007/s00530-022-00943-5 · Cited by 10

Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture LearningPreprint

Huo, Y., Cai, J., Cheng, C., Raju, A., Yan, K., Landman, B. A., Xiao, J. Z., Lü, L., Liao, C., & Harrison, A. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.15691 · Cited by 10

Review of Disentanglement Approaches for Medical Applications: Towards Solving the Gordian Knot of Generative Models in HealthcarePreprint

Fragemann, J., Ardizzone, L., Egger, J., Kleesiek, J., & Workshop, M. (2022) · doi:10.36227/techrxiv.19364897 · Cited by 10

Softgan: Towards Accurate Lung Nodule Segmentation via Soft Mask Supervision

Wang, C., Xu, R., Xu, S., Meng, W., Xiao, J., Peng, Q., & Zhang, X. (2022) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859677 · Cited by 9

IRDNet: Iterative Relation-Based Dual-Domain Network via Metal Artifact Feature Guidance for CT Metal Artifact Reduction

Wang, H., Yang, S., Bai, X. M., Wang, Z., Wu, J., Lv, Y., & Cao, G. (2024) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2024.3424941 · Cited by 9

Machine Learning of Endoscopy Images to Identify, Classify, and Segment Sinonasal Masses

Levi, L., Ye, K., Fieux, M., Renteria, A. E., Lin, S., Xing, L., Ayoub, N., Patel, Z. M., Nayak, J. V., Hwang, P. H., et al. (2025) · International Forum of Allergy & Rhinology · doi:10.1002/alr.23525 · Cited by 9

Classification of Medical Image Notes for Image Labeling by Using MinBERT

Yang, B., Yang, Y., Li, Q., Lin, D., Li, Y., Zheng, J., & Cai, Y. (2023) · Tsinghua Science & Technology · doi:10.26599/tst.2022.9010012 · Cited by 10

CT image reconstruction with SuiteSparseQR factorization package

Chillarón, M., Vidal, V., & Verdú, G. (2019) · Radiation Physics and Chemistry · doi:10.1016/j.radphyschem.2019.04.039 · Cited by 9

Evaluation of image filters for their integration with LSQR computerized tomography reconstruction method

Chillarón, M., Vidal, V., & Verdú, G. (2020) · PLoS ONE · doi:10.1371/journal.pone.0229113 · Cited by 9

Deformable Dilated Faster R-CNN for Universal Lesion Detection in CT Images

Hellmann, F., Ren, Z., André, E., & Schuller, B. W. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9631021 · Cited by 9

Vertical Federated Principal Component Analysis on Feature-Wise Distributed Data

Cheung, Y., Lou, J., & Yu, F. N. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-90888-1_14 · Cited by 9

Böbrek Hastalıkları için Açıklanabilir Yapay Zeka Destekli Derin Öğrenmeye Dayalı Bir Tespit ve Tahmin Modeli

BAYRAM, A. F., GURKAN, C., BUDAK, A., & KARATAŞ, H. (2022) · European Journal of Science and Technology · doi:10.31590/ejosat.1171777 · Cited by 9

DiffULD: Diffusive Universal Lesion Detection

Zhao, P., Li, H., Jin, R., & Zhou, S. K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43904-9_10 · Cited by 11

Bayesian Imaging Using Plug & Play Priors: When Langevin Meets Tweedie

Laumont, R., Bortoli, V. D., Almansa, A., Delon, J., Durmus, A., & Pereyra, M. (2022) · SIAM Journal on Imaging Sciences · doi:10.1137/21m1406349 · Cited by 123

Improving Weakly Supervised Lesion Segmentation using Multi-Task LearningPreprint

Chu, T., Li, X., Vo, H. V., Summers, R. M., & Sizikova, E. (2021) · HAL · Cited by 9

Foundation Models for Quantitative Biomarker Discovery in Cancer ImagingPreprint

Pai, S., Bontempi, D., Hadžić, I., Prudente, V., Sokač, M., Chaunzwa, T. L., Bernatz, S., Hosny, A., Mak, R. H., Birkbak, N. J., et al. (2023) · medRxiv · doi:10.1101/2023.09.04.23294952 · Cited by 9

Technological Advancements in Augmented, Mixed, and Virtual Reality Technologies for Surgery: A Systematic Review

Sang, A. Y., Wang, X., & Paxton, L. (2024) · Cureus · doi:10.7759/cureus.76428 · Cited by 11

Enhancing polyp detection in endoscopy with cross-channel self-attention fusion

Liang, X., Chen, S., Shu, L., Wang, D., Chen, Q., Cao, Y., Liu, B., Zhang, H., & Liu, X. (2025) · Smart Health · doi:10.1016/j.smhl.2025.100578 · Cited by 8

Encryption Method for JPEG Bitstreams for Partially Disclosing Visual Information

Hirose, M., Imaizumi, S., & Kiya, H. (2024) · Electronics · doi:10.3390/electronics13112016 · Cited by 8

Efficient Neural Network Image Reconstruction from Raw Data Using a Radon Inversion Layer

Whiteley, W., & Gregor, J. (2019) · IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) · doi:10.1109/nss/mic42101.2019.9059967 · Cited by 8

TauMed: test augmentation of deep learning in medical diagnosis

Hou, Y., Liu, J., Wang, D., He, J., Fang, C., & Chen, Z. (2021) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3460319.3469080 · Cited by 8

Convergent–Diffusion Denoising Model for multi-scenario CT Image Reconstruction

Ma, X., Zou, M., Fang, X., Luo, G., Wang, W., Dong, S., Li, X., Wang, K., Dong, Q., Tian, Y., et al. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102491 · Cited by 8

Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction

Ma, C., Li, Z., Li, Y., Han, J. T., Zhang, J., Zhang, Y., Liu, J., & Shan, H. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3535906 · Cited by 7

Intraoperative stenosis detection in X-ray coronary angiography via temporal fusion and attention-based CNN

Chen, M., Wang, S., Liang, K., Chen, X., Xu, Z., Zhao, C., Yuan, W., Wan, J., & Huang, Q. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102513 · Cited by 7

A learned filtered backprojection method for use with half-time circular radon transform data

Çam, R. M., Villa, U., & Anastasio, M. A. (2022) · SPIE Medical Imaging: Physics of Medical Imaging · doi:10.1117/12.2612941 · Cited by 7

An improved dual-domain network for metal artifact reduction in CT images using aggregated contextual transformations

Tang, H. N., Jiang, S., Lin, Y., Li, Y., & Bao, X. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aced78 · Cited by 7

SAFPN: a full semantic feature pyramid network for object detection

Wang, G., Li, Q., Wang, N., & Liu, H. (2023) · Pattern Analysis and Applications · doi:10.1007/s10044-023-01200-9 · Cited by 7

MVDI25K: A large-scale dataset of microscopic vaginal discharge images

Li, L., Liu, J., Yu, F., Wang, X., & Xiang, T.-Z. (2021) · BenchCouncil Transactions on Benchmarks Standards and Evaluations · doi:10.1016/j.tbench.2021.100008 · Cited by 7

Research on lung nodule recognition algorithm based on deep feature fusion and MKL-SVM-IPSO

Li, Y., Zheng, H., Huang, X., Chang, J., Hou, D., & Lu, H. (2022) · Scientific Reports · doi:10.1038/s41598-022-22442-3 · Cited by 10

Enhancing thoracic disease detection using chest X-rays from PubMed Central Open Access

Lin, M., Hou, B., Mishra, S., Yao, T., Huo, Y., Yang, Q., Wang, F., Shih, G., & Peng, Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106962 · Cited by 7

MLANet: Multi-Layer Anchor-free Network for generic lesion detection

Liu, Z., Xie, X., Song, Y., Zhang, Y., Liu, X., Zhang, J., & Sheng, V. S. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104255 · Cited by 7

Automatic recognition of abdominal lymph nodes from clinical text

Peng, Y., Lee, S.-W., Elton, D. C., Shen, T. C., Tang, Y., Chen, Q., Wang, S., Zhu, Y., Summers, R. M., & Lu, Z. (2020) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/2020.clinicalnlp-1.12 · Cited by 7

A New Window Loss Function for Bone Fracture Detection and Localization in X-ray Images with Point-based AnnotationPreprint

Zhang, X., Wang, Y., Cheng, C., Lü, L., Harrison, A. P., Xiao, J. Z., Liao, C., & Miao, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.04066 · Cited by 7

LNDb v4: pulmonary nodule annotation from medical reports

Ferreira, C. A., Sousa, C., Marques, I. D., de Sousa, P. H. P., Ramos, I. M., Coimbra, M. T., & Campilho, A. (2024) · Scientific Data · doi:10.1038/s41597-024-03345-6 · Cited by 7

A graph-theoretic approach for the analysis of lesion changes and lesions detection review in longitudinal oncological imaging

Veroli, B. D., Lederman, R., Shoshan, Y., Sosna, J., & Leo Joskowicz, B. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103268 · Cited by 6

Automatic 3D pelvimetry framework in CT images and its validation

Shao, J., Wu, Q. H., Zhang, Y., Liu, C., Huo, X., & Wang, C. (2024) · Scientific Reports · doi:10.1038/s41598-024-72123-6 · Cited by 6

Attention-Guided Pancreatic Duct Segmentation from Abdominal CT Volumes

Shen, C., Roth, H. R., Yuichiro, H., Oda, M., Miyamoto, T., Sato, G., & Mori, K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-90874-4_5 · Cited by 6

Deep learning for abdominal adipose tissue segmentation with few labelled samples

Wang, Z., Hounye, A. H., Zhang, J., Hou, M., & Qi, M. (2021) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-021-02533-8 · Cited by 6

Detecting Absence of Bone Wall in Jugular Bulb by Image Transformation Surrogate Tasks

Li, X., Zhou, Y., Yin, H., Wang, Z., Li, Z., & Zhang, H. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3139917 · Cited by 6

Supply Chain Based Demand Analysis of Different Deep Learning Methodologies for Effective Covid-19 Detection

Srujana, K. S., Kashyap, S. N., Shrividhiya, G., Gururaj, C., & Induja, K. S. (2022) · Studies in systems, decision and control · doi:10.1007/978-981-19-0240-6_9 · Cited by 6

Bayesian statistics‐guided label refurbishment mechanism: Mitigating label noise in medical image classification

Gao, M., Feng, X., Geng, M., Jiang, Z., Zhu, L., Meng, X., Zhou, C., Ren, Q., & Lu, Y. (2022) · Medical Physics · doi:10.1002/mp.15799 · Cited by 6

Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction

Xie, W., & Blaschko, M. B. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43907-0_8 · Cited by 6

Visual Grounding of Whole Radiology Reports for 3D CT Images

Ichinose, A., Hatsutani, T., Nakamura, K., Kitamura, Y., Iizuka, S., Simo-Serra, E., Kido, S., & Tomiyama, N. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-43904-9_59 · Cited by 6

Phase identification for dynamic CT enhancements with generative adversarial network

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Two-stage deep learning network-based few-view image reconstruction for parallel-beam projection tomography

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Limited View Tomographic Reconstruction Using a Deep Recurrent Framework with Residual Dense Spatial-Channel Attention Network and Sinogram ConsistencyPreprint

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Review of Disentanglement Approaches for Medical Applications: Towards Solving the Gordian Knot of Generative Models in Healthcare

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Hybrid model-based and deep learning-based metal artifact reduction method in dental cone-beam computed tomography

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A hybrid training database and evaluation benchmark for assessing metal artifact reduction methods for X‐ray CT imaging

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Large-Scale Foundation Models for Radiological Image Analysis: Clinical Applications, Technical Challenges, and Future Directions

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Learning with less: A survey of deep learning in medical imaging under varying supervision levels

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Foundation Models for Volumetric Medical Imaging: Opportunities, Challenges, and Future Directions

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Development of a chest X-ray machine learning convolutional neural network model on a budget and using artificial intelligence explainability techniques to analyze patterns of machine learning inference

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A promptable CT foundation model for solid tumor evaluation

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Computing a projection operator onto the null space of a linear imaging operator: tutorial

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

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Universal Lesion Detection by Learning from Multiple Heterogeneously Labeled DatasetsPreprint

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

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

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

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