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

Progressively Prompt-Guided Models for Sparse-View CT Reconstruction

Li, J., Du, W., Cui, H., Chen, H., Zhang, Y., & Yang, H. (2024) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2024.3512172 · Cited by 1

Ambient-denoising diffusion generative-adversarial networks for establishing stochastic-object models from noisy-image data

Xu, X., Chen, W., & Zhou, W. (2025) · SPIE Medical Imaging: Image Perception, Observer Performance, and Technology Assessment · doi:10.1117/12.3047936 · Cited by 2

Leveraging Deep Pre-trained Networks for Advanced Skin Lesion Classification for Human Monkeypox Detection

Nagrath, M., Chaudhary, P., & Sharma, M. (2025) · International Journal of experimental research and review · doi:10.52756/ijerr.2025.v47.012 · Cited by 1

A Two-Phase Deep Learning Approach for Architectural Distortion Detection in Mammograms

Ibrahim, S. E., Mabrouk, M. S., Mohamed, W. A., & Elnokrashy, A. F. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01613-4 · Cited by 1

Hierarchical Contrastive Learning for Precise Whole-Body Anatomical Localization in PET/CT Imaging

Gao, Y., Shu, Y., Yu, M., Chen, Y., Liu, J., Zhong, S., Zhang, W., Zhan, Y., Zhou, X. S., Wang, X., et al. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3599197 · Cited by 2

Ensemble Deep Learning Approaches for Skin Cancer Detection and Prediction

Kingsly, C. S., Lakshmi, A. A., Ahila, A., Nallasivan, G., Anitha, A., Nisha, M. S., Ponkumar, D. D. N., Franklin, N. M., Roselin, V., & Saravanakumar, R. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-2329-7_19 · Cited by 2

Chest X-Ray Pneumonia Detection Using Federated Learning : *Privacy-Preserving Deep Learning for Medical Image Classification

Archana, K. S., Shah, V. H., & Joseph, K. (2025) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit65521.2025.11166991 · Cited by 1

Ontology-regularized hierarchical transformers for anatomy and phenotype-aware medical image retrieval

Yan, T. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00372-0 · Cited by 1

Implicit Augmentation Contrastive Learning for long-tailed medical image classification

Deng, X., Wang, X., Zhang, D., Zhen, S., Ma, S., & Wang, Y. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110648 · Cited by 1

Evaluation of No-Reference Image Quality Measurements for Medical Imaging

Özer, D., & Güveniş, A. (2025) · Medical Technologies Congress (TIPTEKNO) · doi:10.1109/tiptekno68206.2025.11270146 · Cited by 1

Unsupervised anomaly detection in brain MRI via disentangled anatomy learning

Yang, T., Wang, X., Liu, H., Gong, G., Wu, L., Wang, Y., & Wang, L. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103922 · Cited by 1

Accurate detection of kidney stones using attention-based graph models

Zavvar, M., & Ebrahimnezhad, H. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae35e1 · Cited by 1

MedCDA: Counterfactual Data Augmentation for Medical Image Analysis

Yao, K., & Zhao, J. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-5631-1_32 · Cited by 1

Review of generative AI for lesion localization and automatic report generation

Sun, K., Wang, Y., Huang, S., Liu, Y., Chen, H., Lian, Z., Tan, M., Lin, X., Zhou, W., Qiu, Z., et al. (2026) · Med-X · doi:10.1007/s44258-026-00076-8 · Cited by 1

Include-Slim: Supporting Similarity Retrieval Variants With a Metric Access Method

Silva, W., Traina, A. J. M., & Traina, C. (2026) · IEEE Open Journal of the Computer Society · doi:10.1109/ojcs.2026.3687006 · Cited by 1

SDPT: Synchronous Dual Prompt Tuning for Visual-Language Pre-Trained Models

Zhou, Y., Wu, Y., Saiyin, J., Wei, B., & Xu, Y. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3692711 · Cited by 1

All-In-One medical image restoration via Text-Guided prompting and Dual-Domain modeling

Cui, J., Liu, B., Wu, X., Zhou, J., Zhou, L., Shen, D. G., & Wang, Y. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104558 · Cited by 1

A review of small object detection in the deep learning era advancements, challenges and future trends

Avhad, M., Kamat, P. V., Kumar, S., & Sayyad, S. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01751-w · Cited by 2

Language-Guided Segmentation of Medical Images: A Review of Foundation Models

Qamar, S. (2026) · Bioengineering · doi:10.3390/bioengineering13070803 · Cited by 1

MobileMamba-HC: Medical Image Disease Detection in Healthcare Integrating Frequency Adaptive Dilated Convolution and Spatial-Channel Synergistic Attention

Dai, Y. (2026) · Innovations in Applied Engineering and Technology · doi:10.62836/iaet.v5i1.0002 · Cited by 1

Lesion Detection with Deep Aggregated 3D Contextual Feature and Auxiliary Information

Zhang, H., & Chung, A. C. S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32692-0_6 · Cited by 1

Automatic Kidney Lesion Detection for CT Images Using Morphological CNN

Archana, P., Chethan, S., Chiranth., M., N, J. R. K., & Sanketha. Gowda., A. (2021) · International Journal of Advanced Research in Computer Science · doi:10.26483/ijarcs.v12i0.6734 · Cited by 1

Artificial Intelligence for Medical Diagnosis

Richens, J. G., & Buchard, A. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_29-1 · Cited by 1

Examining the Validity of Input Lung CT Images Submitted to the AI-Based Computerized Diagnosis

Kosareva, A. A., Paulenka, D. A., Snezhko, E. V., Bratchenko, I. А., & Kovalev, V. A. (2022) · Journal of Biomedical Photonics & Engineering · doi:10.18287/jbpe22.08.030307 · Cited by 1

Separable vs. End-to-End Learning: A Critical Examination of Learning Paradigms

Baxter, J. S. H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-23223-7_3 · Cited by 1

Reconstruction: unsupervised artifact reduction

Liao, H., Zhou, S. K., & Luo, J. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-824383-1.00017-4 · Cited by 1

FSD: Fully-Specialized Detector via Neural Architecture Search

Huang, Z., & Li, Y. (2023) · International Conference on Computer Communication and the Internet (ICCCI) · doi:10.1109/iccci59363.2023.10210167 · Cited by 1

Three-Dimensional Velocity Field Interpolation Based on Attention Mechanism

Yao, X., Cui, M., Lian, W., Li, Y., Zhou, C., Su, M., & Hu, G. (2023) · Applied Sciences · doi:10.3390/app132413045 · Cited by 1

One model, two brains: Automatic fetal brain extraction from MR images of twins

Chen, J., Lu, R., Jing, B., Zhang, H., Shen, D. G., & Shen, D. G. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102330 · Cited by 1

Automatic Lung Nodules Detection Using a Modified YOLOv5

Bhatt, S. D., Astik, M. B., & Soni, H. B. (2024) · Journal of biomimetics, biomaterials and biomedical engineering · doi:10.4028/p-kbni1t · Cited by 1

Stability of artificial intelligence methods in computed tomography metal artifacts reduction task

Gilmanov, M., Kirkicha, A. A., Ingacheva, A. S., Chukalina, M. V., & Nikolaev, D. (2025) · Symposium on Pattern Recognition and Applications (SPRA) · doi:10.1117/12.3056526 · Cited by 1

A Hierarchical Slice Attention Network for Appendicitis Classification in 3D CT Scans

Huang, C.-W., Hwai, H., Lee, C., & Wu, P.-Y. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10981299 · Cited by 1

MPVT+: a noise-robust training framework for automatic liver tumor segmentation with noisy labels

Cheng, X., Tian, H., Zhou, J., Xie, T., Gong, H., Liu, M., Wei, Y., & Lü, W. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1653865 · Cited by 1

Parallel CT Reconstruction for Multiple Slices Studies with SuiteSparseQR Factorization Package

Chillarón, M., Vidal, V., & Verdú, G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22744-9_12 · Cited by 1

Radiomics Toolkit

Fintelmann, F. J., Troschel, A. S., & Chiles, C. (2019) · Journal of Thoracic Imaging · doi:10.1097/rti.0000000000000443 · Cited by 1

Multi-scale Convolutional Neural Network Based on 3D Context Fusion for Lesion Detection

Wu, Z., Chen, J., Wang, Z., Su, J., & Cai, G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31654-9_49 · Cited by 1

Generalized Lesion Detector Based on Convolutional Neural Network

Wu, H., & Jian-Zhi, D. (2019) · International Conference on Algorithms, Computing and Artificial Intelligence (ACAI) · doi:10.1145/3377713.3377746 · Cited by 1

A Case Study of Transfer of Lesion-Knowledge

Krishnan, S., Khincha, R., Vig, L., Dash, T., & Srinivasan, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61166-8_15 · Cited by 1

A crowdsourcing framework for retinal image semantic annotation and report documentation with deep learning enhancement

Shao, J., Li, J., Kong, W., Liu, S., Wu, J., & Wu, H. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1955/1/012037 · Cited by 1

A Coarse Feature Reuse Deep Neural Network for CXR Lesion Detection

Yang, X., Li, X., Shen, L., Cao, M., & Zhou, C. (2021) · International Conference on Information Technology in Medicine and Education · doi:10.1109/itme53901.2021.00070 · Cited by 1

Virtual non-metal network for metal artifact reduction in the sinogram domain

Choi, D.-I., Kwon, T., Hwang, J., Hwang, J. I., Choi, Y., & Cho, S. (2022) · International Conference on Image Formation in X-Ray Computed Tomography (CT Meeting) · doi:10.1117/12.2646950 · Cited by 1

Survey of the Detection and Classification of Pulmonary Lesions via CT and X-RayPreprint

Sun, Y., Li, C., Zhang, Q., Zhou, A., & Zhang, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.15442 · Cited by 1

Multi-Disease Classification of 13,667 Body CT Scans Using Weakly Supervised Deep LearningPreprint

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 1

Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical DistributionPreprint

Vardanyan, E., Hunanyan, S., Galstyan, T., Minasyan, A., & Dalalyan, A. S. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2307.16422 · Cited by 1

Transparency in Radiology and Nuclear Medicine AI Research: Realistic Expectation or Pipedream?

Summers, R. M. (2023) · Academic Radiology · doi:10.1016/j.acra.2023.07.026 · Cited by 1

Deep Learning in Surgical Assistance

Oda, M. (2020) · Journal of Japan Society of Computer Aided Surgery · doi:10.5759/jscas.22.54

Found in Translation: Unpacking the Artificial Intelligence Revolution That Has Already Arrived

Woloshyn, O., Preiss, K., Rowe, S. P., Fishman, E. K., & Chu, L. C. H. (2020) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.02.018

A Study of Coronary Artery Disease in the Left Circumflex Artery via Artificial Vessel RestorationPreprint

Paulraj, A., & Liu, F. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.27.119628

Towards Scalable Image Classifier Learning with Noisy Labels via Domain Adaptation

Lee, K.-H., He, X., Yang, L., & Zhang, L. (2020) · Springer eBooks · doi:10.1007/978-3-030-45529-3_9

Station number assignment to abdominal lymph node for assisting gastric cancer surgery

Hayashi, Y., Misawa, K., & Mori, K. (2020) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2020.1835543

FPAENet: Pneumonia Detection Network Based on Feature Pyramid Attention EnhancementPreprint

Zhang, X., Wang, B., Yuan, D., Xu, Z., & Xu, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.08706

Lesion-Inspired Denoising Network: Connecting Medical Image Denoising and Lesion DetectionPreprint

Chen, K., Long, K., Ren, Y., Sun, J., & Pu, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.08845

Learning To Recover Sharp Detail From Simulated Low-Dose Ct Studies

Cole, P., Pyrros, A. T., & Koyejo, S. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434011

Artificial intelligence for imaging in immunotherapy

Trebeschi, S. (2021) · Research Publications (Maastricht University) · doi:10.26481/dis.20210322st

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) · medRxiv · doi:10.1101/2021.06.02.21258256

Dual Skip Connections Minimize the False Positive Rate of Lung Nodule Detection in CT images

Xu, J., Ernst, P., Liu, T. L., & Nürnberger, A. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630096

3D Adaptive SMCM Filtering of CT Images

Draganov, I. R., & Mironov, R. (2021) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs53288.2021.9660875

Análisis y desarrollo de algoritmos de altas prestaciones para reconstrucción de imagen médica TAC 3D basados en la reducción de dosis.

Chillarón, M. (2021) · doi:10.4995/thesis/10251/180116

Deep Monochromatic Metal Artifact Reduction for Computed Tomography

Song, S. S. (2021) · International Conference on Digital Medicine and Image Processing (DMIP) · doi:10.1145/3506651.3506653

Bone lesion detection using attention mechanism and multi-scales feature fusion network

Bai, Z., Fang, C., & Liu, X. (2021) · China Automation Congress (CAC) · doi:10.1109/cac53003.2021.9727250

Colorectal segmentation in medical images

Xie, Y. (2022) · International Conference on Sensors and Information Technology (ICSI) · doi:10.1117/12.2637414

Radiomics analysis and metastatic patients: can we really continue to sidestep intra-patient tumor heterogeneity ?Preprint

Henry, T., Sun, R., Lerousseau, M., Estienne, T., Robert, C., Besse, B., Robert, C., Nikos Paragios, A., & Deutsch, É. (2022) · Research Square · doi:10.21203/rs.3.rs-1775619/v1

Liver Segmentation Quality Control in Multi-sequence MR Studies

Wang, Y., & Palma, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18814-5_6

Ethical and Legal Risks of Artificial Intelligence in Radiology

D’Antonoli, T. A. (2022) · Springer eBooks · doi:10.1007/978-3-030-99680-2_12

Exploiting Liver CT scans in Colorectal Carcinoma genomics mutation classification

Perlo, D., Berton, L., Delpiano, A., Menchini, F., TIBALDI, S., Grosso, M., & Fonio, P. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020613

Pre-text Representation Transfer for Deep Learning with Limited and Imbalanced Data: Application to CT-Based COVID-19 Detection

Altaf, F., Islam, S. M. S., Janjua, N. K., & Akhtar, N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25825-1_9 · Cited by 1

Fine-Tuned Faster R-CNN for Universal Lesion Detection

Erzen, E. M., & Bütün, E. (2022) · International Conference on Data Analytics for Business and Industry (ICDABI) · doi:10.1109/icdabi56818.2022.10041608

Handling Domain Shift for Lesion Detection via Semi-supervised Domain Adaptation

Sheoran, M., Sharma, M., Dani, M., & Vig, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-27066-6_8

Object Detection in Medical Imaging: A Quantitative and Qualitative Analysis of the Scientific Research (Preprint)Preprint

Albuquerque, C., Henriques, R., & Castelli, M. (2023) · doi:10.2196/preprints.47462

Correcting class imbalances with self-training for improved universal lesion detection and tagging

Shieh, A., Mathai, T. S., Liu, J., Paul, A., & Summers, R. M. (2023) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2655267

GÜÇLENDİRİLMİŞ GRADYAN MİNİMİZASYONU KULLANARAK MEDİKAL GÖRÜNTÜLERDE GÜRÜLTÜ ARINDIRMA

Ertaş, M. (2023) · Uludağ University Journal of The Faculty of Engineering · doi:10.17482/uumfd.1139249

Uretery Stone Detection with CT Scan Image Contrast Analysis

Sulaksono, N., Adi, K., Isnanto, R. R., & Warsito, B. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-0248-4_12

A universal lesion detection method based on partially supervised learning

Wang, X., Shi, X., Meng, X., Zhang, Z., & Zhang, C. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1084155

Optimizing the Non-local Means Filtering of CT Images

Draganov, I. R., & Gаnchеvа, V. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-6775-6_1

A Modified Tseng Algorithm Approach to Restoring Thoracic Diseases’ Computerized Tomography ImagesPreprint

Umar, H., Adamu, A., Hijaz, A., & Ozsahin, D. U. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.27.573395

CPR-Net: A Novel Joint Learning Network for Pulmonary Nodule Evaluation

Gong, C., Liu, J., & Li, C.-Q. (2023) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53992.2023.10394045

Ultrasound transducer induced CT artifacts correction in ultrasound-guided proton therapy for cardiac arrhythmia

Puvanasunthararajah, S. (2023) · Queensland University of Technology · doi:10.5204/thesis.eprints.246105

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

Liu, L., Liu, J., Mukherjee, P., Anand, A., Zhu, Y., Santra, B., & Summers, R. M. (2024) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.3008785

The Wireless Chest-Patch RF Sensors and Measurement System for Assessing Fluid Accumulation in Lungs: A Gateway to Discovering the Mysteries of the Lung

Iskander, M. F., Leong, C., Xiao, Y., & Yun, Z. (2024) · Springer eBooks · doi:10.1007/978-3-031-39824-7_25

[Identifying Novel Coronavirus Pneumonia With CT Images: A Deep Learning Approach With Detail Upsampling and Attention Guidance].

Chen, J., Chen, R., Qiu, J., Jin, Y., & Zhang, L. (2024) · PubMed Central · doi:10.12182/20240360605

Machine learning classification of retinal imaging of neuropsychiatric and healthy cases in a subset of the UK Biobank cohort

Aideyan, B. (2023) · Digital Repository Service (Northeastern University) · doi:10.17760/d20652988

Learning a stable approximation of an existing but unknown inverse mapping: application to the half-time circular Radon transform

Çam, R. M., Villa, U., & Anastasio, M. A. (2024) · Inverse Problems · doi:10.1088/1361-6420/ad4f0a

Detection of Liver Disease using ANN

Hemanth, B., Rajakumar, M., Raghavendra, H. V., & Thampi, D. P. (2024) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-18821

PDSE: A Multiple Lesion Detector for CT Images Using PANet and Deformable Squeeze-and-Excitation Block

Fan, D., Yu, H., & Xu, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-66955-2_18

A Feature-Guided Dual-Domain Network for CT Metal Artifact Reduction

Wang, H., Wang, Z., Yang, S., Bai, X. P., Wu, J., Lv, Y., & Cao, G. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635638

φ-net-based few-views reconstruction for sparse optical projection tomography

Wang, N., Huang, P., Wang, H., Wang, L., Xie, H. M., Chen, D., Ren, S., Cai, H., Wang, X., Zhu, S., et al. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106678

Imaging Tumor Metabolism and Its Heterogeneity: Special Focus on Radiomics and AI

Papp, L., Haberl, D., Ecsedi, B., Hatt, M., & Lopci, E. (2024) · Interdisciplinary cancer research · doi:10.1007/16833_2024_427

F2IFlow for CT Metal Artifact Reduction

Su, J., Wang, C., Li, Y., Liang, D., & Shang, K. (2024) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2024.3485538

Deep Learning Classifiers to Reduce False Positives in Osteolytic Lesion Segmentation Results from Low-Dose CT Scans of Multiple Myeloma

van Leeuwen, M. P., Jadikar, M., van Oudheusden, T. R., Oei, S. P., Steunenberg, B. E., Kint, R., Ranschaert, E. R., Bosma, G. P. T., Saygılı, G., & Ong, L. S. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-74650-5_9

Connected Health: IoT and AI-driven CNNs for Lung Cancer Early Detection

Justin, S., & Selvan, T. (2024) · International Journal of Electronics and Communication Engineering · doi:10.14445/23488549/ijece-v11i12p125

Tumor Spread Estimation in lungs using Medical Image Segmentation based on Deep Neural Networks

Kumar, V., & Kavya, B. (2024) · International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC) · doi:10.1109/icec59683.2024.10837064

Benchmark image and clinical datasets for analysis in the medical system

Narayanan, S., Anbarasi, L. J., Sharon, J. J., Neeraja, R., & Gabriel, J. J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-32862-6.00003-1

ENHANCING LESION DETECTION IN CT IMAGING-A MORPHOLOGICAL CNN APPROACH WITH MULTI-IOU THRESHOLDING

Nagarajan, S., & Ramprasath, M. (2025) · International Journal for Multiscale Computational Engineering · doi:10.1615/intjmultcompeng.2025056361

Evaluating the robustness of features generated by a foundation model from CT with different reconstruction parameters

Park, S. H., Yadav, A., Tabatabaei, H., Lin, Y., Aberle, D. R., Prosper, A. E., & Hsu, W. W. Y. (2025) · SPIE Medical Imaging: Imaging Informatics for Healthcare, Research, and Applications · doi:10.1117/12.3049089

Novel Deep Learning Approach for Tooth Segmentation in Orthodontic Treatment Using STU-RestNet

Kapoor, S., & Maheshwary, P. (2025) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt64827.2025.10968544

Predictive Modeling of Lung Water Content with Machine Learning and RF Sensor

Ganguly, P., Das, S., Chakrabarti, A., & Siddiqui, J. Y. (2024) · IEEE Conference on Antenna Measurements and Applications (CAMA) · doi:10.1109/cama62287.2024.10986198

A comprehensive review of multi-view datasets and applications

hajri, F., Fradi, H., & Mahjoub, M. A. (2026) · The Visual Computer · doi:10.1007/s00371-026-04565-0

SwinDDnet: A Dual Domain Network Based on Swin Transformer for CT Metal Artifact Reduction

Zheng, S., Zhang, D., Yu, C., Zhu, L., Huang, Z., Zhu, D., Jia, L., & Yu, H. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6948-6_15

Research Progress on Nucleic Acid Information Material

Song, L., Li, M., & Zuo, X. (2025) · 中国科学基金 · doi:10.3724/bnsfc-2025.02.19.0002

U.S. policy and planning for scientific data development

ZHANG, Y., Li, X., Kang, J., & Yufang, M. I. N. (2025) · China Scientific Data · doi:10.11922/11-6035.csd.2023.0138.zh

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