Contrastive Learning with Continuous Proxy Meta-data for 3D MRI Classification

Densely Connected Convolutional Networks

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PyTorch: An Imperative Style, High-Performance Deep Learning Library

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Momentum Contrast for Unsupervised Visual Representation Learning

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Representation Learning with Contrastive Predictive Coding

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Context Encoders: Feature Learning by Inpainting

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Dimensionality Reduction by Learning an Invariant Mapping

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Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning

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Supervised Contrastive Learning

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Improved regularization of convolutional neural networks with cutout

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Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles

Noroozi, M., & Favaro, P. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_5 · Cited by 2,775 · see citing works

Unsupervised Visual Representation Learning by Context Prediction

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Unsupervised Learning of Visual Features by Contrasting Cluster Assignments

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Unsupervised Representation Learning by Predicting Image Rotations

Gidaris, S., Singh, P., & Komodakis, N. (2018) · arXiv · doi:10.48550/arxiv.1803.07728 · Cited by 2,477 · see citing works

Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

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A simple framework for contrastive learning of visual representations

Chen, T. (2024) · TIB Data Manager · doi:10.57702/iuuvgtaz · Cited by 1,140 · see citing works

What makes for good views for contrastive learning

Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., & Isola, P. (2020) · arXiv · doi:10.48550/arxiv.2005.10243 · Cited by 773 · see citing works

Contrastive learning of global and local features for medical image segmentation with limited annotationsPreprint

Chaitanya, K., Erdil, E., Karani, N., & Konukoglu, E. (2020) · Neural Information Processing Systems · doi:10.3929/ethz-b-000443425 · Cited by 720 · see citing works

Self-supervised learning for medical image analysis using image context restoration

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Debiased Contrastive LearningPreprint

Chuang, C.-Y., Robinson, J., Lin, Y.-C., Torralba, A. B., & Jegelka, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00224 · Cited by 395 · see citing works

Models Genesis

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Vicinal Risk Minimization

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Bipolar and Schizophrenia Network for Intermediate Phenotypes: Outcomes Across the Psychosis Continuum

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Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik’s Cube

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3D Self-Supervised Methods for Medical ImagingPreprint

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Revisiting Rubik’s Cube: Self-supervised Learning with Volume-Wise Transformation for 3D Medical Image Segmentation

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Neurodevelopmental subtypes of bipolar disorder are related to cortical folding patterns: An international multicenter study

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CO2: Consistent Contrast for Unsupervised Visual Representation LearningPreprint

Wei, C., Wang, H., Shen, W., & Yuille, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.02217 · Cited by 38 · see citing works

Lithium prevents grey matter atrophy in patients with bipolar disorder: an international multicenter study

Hozer, F., Sarrazin, S., Laidi, C., Favre, P., Pauling, M., Cannon, D. M., McDonald, C., Emsell, L., Mangin, J., Duchesnay, É., et al. (2020) · Psychological Medicine · doi:10.1017/s0033291719004112 · Cited by 33 · see citing works

Image Generation Using Continuous Conditional Generative Adversarial Networks

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Self-supervised learning for medical image classification: a systematic review and implementation guidelines

Huang, S.-C., Pareek, A., Jensen, M., Lungren, M. P., Yeung, S., & Chaudhari, A. S. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00811-0 · Cited by 479 · see citing works

Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging

Wolf, D., Payer, T., Lisson, C. S., Lisson, C. G., Beer, M., GOETZ, M. A., & Ropinski, T. (2023) · Scientific Reports · doi:10.1038/s41598-023-46433-0 · Cited by 64 · see citing works

A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound

Vanberlo, B., Hoey, J., & Wong, A. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01253-0 · Cited by 59 · see citing works

Hierarchical Self-Supervised Learning for 3D Tooth Segmentation in Intra-Oral Mesh Scans

Liu, Z., He, X., Wang, H., Xiong, H., Zhang, Y., Wang, G., Hao, J., Feng, Y., Zhu, F., & Hu, H. R. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3222388 · Cited by 53 · see citing works

SC-SSL: Self-Correcting Collaborative and Contrastive Co-Training Model for Semi-Supervised Medical Image Segmentation

Miao, J., Zhou, S.-P., Zhou, G., Wang, K.-N., Yang, M., Zhou, S.-J., & Chen, Y. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3336534 · Cited by 41 · see citing works

Contrastive Functional Connectivity Graph Learning for Population-based fMRI Classification

Wang, X., Yao, L., Rekik, I., & Zhang, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16431-6_21 · Cited by 37 · see citing works

Advancements in Medical Radiology Through Multimodal Machine Learning: A Comprehensive Overview

Haq, I. U., Mhamed, M., Alharbi, M., Osman, H., Hamd, Z. Y., & Liu, Z. (2025) · Bioengineering · doi:10.3390/bioengineering12050477 · Cited by 30 · see citing works

Multi-Template Meta-Information Regularized Network for Alzheimer’s Disease Diagnosis Using Structural MRI

Han, K., Li, G., Fang, Z., & Yang, F. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3344384 · Cited by 29 · see citing works

Self-supervised learning of neighborhood embedding for longitudinal MRI

Ouyang, J., Zhao, Q., Adeli, E., Zaharchuk, G., & Pohl, K. M. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102571 · Cited by 29 · see citing works

Evaluation of transfer learning methods for detecting Alzheimer’s disease with brain MRI

Dhinagar, N. J., Thomopoulos, S. I., Rajagopalan, P., Stripelis, D., Ambite, J. L., Steeg, G. V., & Thompson, P. M. (2023) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1117/12.2670457 · Cited by 24 · see citing works

Contrastive Learning for Regression in Multi-Site Brain Age Prediction

Barbano, C. A., Dufumier, B., Duchesnay, É., Grangetto, M., & Gori, P. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230733 · Cited by 22 · see citing works

Brain-Aware Replacements for Supervised Contrastive Learning in Detection of Alzheimer’s Disease

Seyfioğlu, M. S., Liu, Z., Kamath, P. A., Gangolli, S., Wang, S., Grabowski, T. J., & Shapiro, L. G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16431-6_44 · Cited by 19 · see citing works

Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer’s disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI

Chattopadhyay, T., Ozarkar, S. S., Buwa, K., Joshy, N. A., Komandur, D., Naik, J., Thomopoulos, S. I., Steeg, G. V., Ambite, J. L., & Thompson, P. M. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1387196 · Cited by 18 · see citing works

Attention-guided neural network for early dementia detection using MRS data

kherchouche, A., Ahmed, O. B., Guillevin, C., Tremblais, B., Julian, A., Fernández-Maloigne, C., & Guillevin, R. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102074 · Cited by 18 · see citing works

Medical Image Computing and Computer Assisted Intervention – MICCAI 2021

de Bruijne, M., Cattin, P. C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., & Essert, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87196-3 · Cited by 18 · see citing works

CLOC: Contrastive Learning for Ordinal Classification with Multi-Margin N-pair Loss

Pitawela, D., Carneiro, G., & Chen, H. (2025) · 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/cvpr52734.2025.01448 · Cited by 17 · see citing works

Contrastive learning in brain imaging

Xu, X., & Wong, S. T. C. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102500 · Cited by 15 · see citing works

An attention-based deep learning network for lung nodule malignancy discrimination

Liu, G., Liu, F., Gu, J., Mao, X., Xie, X., & Sang, J. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.1106937 · Cited by 15 · see citing works

Comparison of Deep Transfer Learning Against Contrastive Learning in Industrial Quality Applications for Heavily Unbalanced Data Scenarios When Data Augmentation Is Limited

Farmanesh, A., Sanchis, R. G., & Ordieres-Meré, J. B. (2025) · Sensors · doi:10.3390/s25103048 · Cited by 13 · see citing works

Multimodal contrastive learning for enhanced explainability in pediatric brain tumor molecular diagnosis

Ketabi, S., Wagner, M. W., Hawkins, C. E., Tabori, U. Y., Ertl-Wagner, B. B., & Khalvati, F. (2025) · Scientific Reports · doi:10.1038/s41598-025-94806-4 · Cited by 12 · see citing works

Unsupervised Representation Learning of Cingulate Cortical Folding Patterns

Chavas, J., Guillon, L., Pascucci, M., Dufumier, B., Rivière, D., & Mangin, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16431-6_8 · Cited by 12 · see citing works

Model Inversion Robustness: Can Transfer Learning Help?

Ho, S.-T., Hao, K. J., Chandrasegaran, K., Nguyen, N.-B., & Cheung, N. M. (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/cvpr52733.2024.01158 · Cited by 9 · see citing works

Learning to Diagnose Cirrhosis from Radiological and Histological Labels with Joint Self and Weakly-Supervised Pretraining Strategies

Sarfati, E., Bône, A., Rohé, M.-M., Gori, P., & Isabelle Bloch, A. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230783 · Cited by 8 · see citing works

Building a General SimCLR Self-Supervised Foundation Model Across Neurological Diseases to Advance 3D Brain MRI Diagnoses

Kaczmarek, E., Szeto, J., Nichyporuk, B., & Arbel, T. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00141 · Cited by 7 · see citing works

HistoMoCo: Momentum Contrastive Learning Pre-Training on Unlabeled Histopathological Images for Oral Squamous Cell Carcinoma Detection

Liao, W., He, Y., Jiang, B., Zhao, J., Gao, M., & Zhang, X. (2025) · Electronics · doi:10.3390/electronics14071252 · Cited by 7 · see citing works

Learning Shape Distributions from Large Databases of Healthy Organs: Applications to Zero-Shot and Few-Shot Abnormal Pancreas Detection

Vétil, R., Nader, C. A., Bône, A., Vullierme, M., Rohé, M.-M., Gori, P., & Isabelle Bloch, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16434-7_45 · Cited by 7 · see citing works

Domain and label efficient approach for diabetic retinopathy severity detection

Ohri, K., & Kumar, M. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16908-3 · Cited by 6 · see citing works

Graph-Based Alzheimer’s Disease Diagnosis with Contrastive Learning and Graph Transformer

Zhang, Y., Cao, G., Xu, X., & Kang, G. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385991 · Cited by 6 · see citing works

Curriculum Based Multi-Task Learning for Parkinson’s Disease Detection

Dhinagar, N. J., Owens‐Walton, C., Laltoo, E., Boyle, C. P., Chen, Y.-L., Cook, P. A., McMillan, C. T., Tsai, C., Wang, J.-J., Wu, Y., et al. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230355 · Cited by 6 · see citing works

Optimizing contrastive learning for cortical folding pattern detection

Gaudin, A., Guillon, L., Fischer, C., Cachia, A., Rivière, D., Mangin, J., & Chavas, J. (2024) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.3006183 · Cited by 5 · see citing works

Video and synthetic MRI pre-training of 3D vision architectures for neuroimage analysis

Dhinagar, N. J., Singh, A., Ozarkar, S. S., Buwa, K. U., Thomopoulos, S. I., Owens‐Walton, C., Laltoo, E., Chen, Y.-L., Cook, P. A., McMillan, C. T., et al. (2024) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.3008837 · Cited by 5 · see citing works

Towards a Foundation Model for Cortical Folding

Laval, J., Chavas, J., Troiani, V., Snyder, W., Patti, M. A., Moyal, M., Plaze, M., Cachia, A., Sun, Z. Y., Frouin, V., et al. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78761-4_8 · Cited by 4 · see citing works

Parameter efficient fine-tuning of transformer-based masked autoencoder enhances resource constrained neuroimage analysis

Dhinagar, N. J., Ozarkar, S. S., Buwa, K. U., Thomopoulos, S. I., Owens‐Walton, C., Laltoo, E., Jagad, C., Chen, Y.-L., Cook, P. A., McMillan, C. T., et al. (2025) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.3047374 · Cited by 4 · see citing works

Turning Waste into Wealth: Leveraging Low-Quality Samples for Enhancing Continuous Conditional Generative Adversarial Networks

Ding, X., Wang, Y., & Xu, Z. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i10.29065 · Cited by 4 · see citing works

Multiview Long-Short Spatial Contrastive Learning For 3D Medical Image Analysis

Cao, G., Wang, Y., Zhang, M., Zhang, J., Kang, G., & Xu, X. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747913 · Cited by 4 · see citing works

Edema Estimation From Facial Images Taken Before and After Dialysis via Contrastive Multi-Patient Pre-Training

Akamatsu, Y., Onishi, Y., Imaoka, H., Kameyama, J., & Tsurushima, H. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3227517 · Cited by 4 · see citing works

Mitigating Bias in MRI-Based Alzheimer’s Disease Classifiers Through Pruning of Deep Neural Networks

Huang, Y.-Y., Chiuwanara, V., Lin, C.-H., & Kuo, P. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45249-9_16 · Cited by 4 · see citing works

Weakly-Supervised Positional Contrastive Learning: Application to Cirrhosis Classification

Sarfati, E., Bône, A., Rohé, M.-M., Gori, P., & Isabelle Bloch, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43907-0_22 · Cited by 3 · see citing works

Margin-aware optimized contrastive learning for enhanced self-supervised histopathological image classification

Gupta, E., & Gupta, V. (2024) · Health Information Science and Systems · doi:10.1007/s13755-024-00316-4 · Cited by 3 · see citing works

CL-ADDA: Contrastive Learning with Amplitude-Driven Data Augmentation for fMRI-Based Individualized Predictions

Liu, J., Xu, L., Guan, Y., Ma, H., & Tian, L. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43907-0_37 · Cited by 3 · see citing works

Decoupled Conditional Contrastive Learning with Variable Metadata for Prostate Lesion Detection

Ruppli, C., Gori, P., Ardon, R., & Isabelle Bloch, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44917-8_9 · Cited by 3 · see citing works

How and Why Does Deep Ensemble Coupled with Transfer Learning Increase Performance in Bipolar Disorder and Schizophrenia Classification?

Petiton, S., Grigis, A., Dufumier, B., & Duchesnay, É. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635777 · Cited by 3 · see citing works

Pre-Training 3D Convolutional Neural Networks for Prodromal Alzheimer's Disease Classification

Jiang, H., & Miao, C. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9891966 · Cited by 3 · see citing works

Semi-weakly Supervised Contrastive Representation Learning for Retinal Fundus ImagesPreprint

Yap, B. P., & Ng, B. K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.02122 · Cited by 3 · see citing works

Conditional Alignment and Uniformity for Contrastive Learning with Continuous Proxy LabelsPreprint

Dufumier, B., Gori, P., Victor, J., Grigis, A., & Duchesnay, É. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.05643 · Cited by 3 · see citing works

Self-supervised learning for early detection of neurodegenerative diseases with small data

Jiang, H. (2023) · doi:10.32657/10356/166402 · Cited by 3 · see citing works

Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical ImagingPreprint

Wolf, D., Payer, T., Lisson, C. S., Lisson, C. G., Beer, M., GOETZ, M. A., & Ropinski, T. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2308.06534 · Cited by 3 · see citing works

Efficient Vision Transformers for Ophthalmic Images Classification: A Comparative Study of Supervised, Semi-Supervised, and Unsupervised Learning Approaches

Al-Wassiti, A. S. A., Mutar, M. T., Sakini, A. S. A., Rasheed, L. S., Yosif, W., Abbas, M. A., Raouf, N. R., & Al‐Shammari, A. S. (2025) · Journal of Medical Systems · doi:10.1007/s10916-025-02292-y · Cited by 2 · see citing works

Not Only Grey Matter: OmniBrain for Robust Multimodal Classification of Alzheimer's Disease

Sharshar, A., Ashraf, Y., Bakr, T., Hassan, S., Elgendy, H., Yaqub, M. K., & Guizani, M. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00140 · Cited by 2 · see citing works

Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging

Grosenick, L., & Liston, C. M. (2026) · Biological Psychiatry · doi:10.1016/j.biopsych.2026.01.023 · Cited by 2 · see citing works

CALM-VLM: Calibration and Selective Prediction in Vision-Language Models for Reliable Brain MRI Classification

Dhinagar, N. J., Jagad, C., Senthilkumar, P., Thomopoulos, S. I., Khan, M. H., Liew, S.-L., the ENIGMA-Stroke Recovery Working Group, Banaj, N., Boric, M. R., Boyd, L. A., et al. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515735 · Cited by 2 · see citing works

WoundNet: A Domain-Adaptable Few-Shot Classification Framework for Wound Healing Assessment

Potlapally, A., Mahajan, S., Briden, M., Shawa, H., Lopez, A. I. M., Yoon, D. J., Mazaheri, A., Yang, H., Dahle, S. E., Isseroff, R. R., et al. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230601 · Cited by 2 · see citing works

MixUp Brain-Cortical Augmentations in Self-supervised Learning

Ambroise, C., Frouin, V., Dufumier, B., Duchesnay, É., & Grigis, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44858-4_10 · Cited by 2 · see citing works

CLCA: Contrastive Learning Using Combined Additional Information for Alzheimer's Diagnosis

Chen, Y., Lou, S., Shuai, M., He, K., & An, Z. (2023) · International Conference on Neural Networks, Information and Communication Engineering (NNICE) · doi:10.1109/nnice58320.2023.10105726 · Cited by 2 · see citing works

Automatic rating of incomplete hippocampal inversions evaluated across multiple cohorts

Hemforth, L., Couvy‐Duchesne, B., Matos, K. D., Brianceau, C., Joulot, M., Banaschewski, T., Bokde, A. L. W., Desrivières, S., Florg, H., Grigis, A., et al. (2024) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2024-3d4e · Cited by 2 · see citing works

An Unsupervised Learning Approach for Pavement Distress Diagnosis via Siamese Networks

Ren, R., Shi, P., Jia, P., & Kim, J. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3500030 · Cited by 2 · see citing works

On the Vulnerability of Skip Connections to Model Inversion Attacks

Hao, K. J., Ho, S.-T., Nguyen, N.-B., & Cheung, N. M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73004-7_9 · Cited by 1 · see citing works

Neuro-AMS: Neuro-Informed Age-Aware and Medical Knowledge-Integrated Strategy for Diagnosis of Multiple Brain Disorders

Zhang, Z., Teng, L., Zhao, N., Liu, Y., Qiu, Z., Weng, Z., Kong, J., Shi, F., & Shen, D. G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05182-0_41 · Cited by 1 · see citing works

Optimizing Transformations for Contrastive Learning in a Differentiable Framework

Ruppli, C., Gori, P., Ardon, R., & Isabelle Bloch, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16760-7_10 · Cited by 1 · see citing works

U-NeTrans at the Edge: Precision and Adaptability in Medical Image Analysis through Segment-based U-Net and Transformer IntegrationPreprint

Iqbal, S., Qureshi, A. N., Alhussein, M. A., Aurangzeb, K., & Wang, H. H. (2024) · Research Square · doi:10.21203/rs.3.rs-3955910/v1 · Cited by 1 · see citing works

Structure-aware neural networks in the study of multi-modal population cohorts : an application to mental health

Ambroise, C. (2024) · doi:10.70675/d2529d35z93e6z4d60zb2c5z752d5f0edda3 · Cited by 1 · see citing works

Improving CXR Self-Supervised Representation by Pretext Task and Cross-Domain Synthetic Data

Chen, S., Wang, Y., Sun, K., & Sun, X. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-99-1145-5_6

Feature-Based Pipeline for Improving Unsupervised Anomaly Segmentation on Medical Images

Frolova, D., Katrutsa, A., & Oseledets, I. V. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44336-7_12

CSIP: Contrastive learning with Graph Neural Network enables chemical structure to image mapping

Liu, L. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/86/20241606

Semi-Supervised Medical Image Segmentation with Cross-View Consistency and Contrastive Learning

Sun, B., Li, K., Liu, J., Sun, Z., Wang, X., Xue, H., Hao, A., Li, S., & Xiao, Y. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822203

Supervised Contrastive Learning for Image-to-Graph Transformers

Banaszak, A., Berger, A. H., Lux, L., Shit, S., Rueckert, D., & Paetzold, J. C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-83243-7_1

PRETI: Patient-Aware Retinal Foundation Model via Metadata-Guided Representation Learning

Lee, Y., Han, W., Jun, Y., Kim, H., Cho, J., & Hwang, S. J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04927-8_50

A Practical Study of Data Requirements for Self-Supervised Learning in Medical Image Analysis

Hommyo, M., Tsuji, T., Kumagai, S., Shiraishi, K., & Kotoku, J. (2025) · Cureus · doi:10.7759/cureus.98049

Tabular Data-Enhanced Multi-modal Alignment and Synthesis for Alzheimer’s Disease Diagnosis

Zhou, W., Liu, Y., Zhang, Y., Sun, K., Li, F., Zhao, S., Wang, Y., & Shen, D. G. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-09569-5_36

Proxy-Label Guided Representation Learning for Brain Imaging Genomics

Lin, X., Liu, A., & Li, Y. (2025) · International Conference on Video, Signal and Image Processing (VSIP) · doi:10.1145/3784713.3784738

Cross-Scanner Reliability of Brain MRI Foundation Model Embeddings: A Travelling-Heads StudyPreprint

González, R. N., Aja‐Fernández, S., Planchuelo‐Gómez, Á., & de Luis-Garcı́a, R. (2026) · medRxiv · doi:10.64898/2026.03.23.26348808

Towards the generation of glioblastoma atlases with deep learning methods : Tumor segmentation and metamorphic image registration

Maillard, M. (2023) · doi:10.70675/fc2a0f91zd2f6z47e5za003z3896c31bed02

Methods and frameworks of annotation cost optimization for deep learning algorithms applied to medical imaging

Ruppli, C. (2023) · theses.fr (ABES) · doi:10.70675/5ee08c9cz5958z4a25zafb4z31040d2ca307

Collateral-Free Learning of Deep Representations : From Natural Images to Biomedical Applications

Barbano, C. A. (2023) · theses.fr (ABES) · doi:10.70675/d0edb18bzae2bz48f8zba04z48d7240e8e0d

Learning pathological representations in neuroimaging : Predicting psychiatric diagnosis by integrating heterogeneity constraints

Louiset, R. (2024) · doi:10.70675/5d07f2feze3fez449bzb611z719495d62239

Exploiting Heterogeneous Labels in Deep Learning for Medical Image Analysis : Application to the Automated Diagnosis of Liver Diseases

Sarfati, E. (2025) · theses.fr (ABES) · doi:10.70675/c8de34e7z1fa6z42cbza183z1e37c1b52362

Automatic discovery of disease subgroups by contrasting with healthy controls

Louiset, R., Duchesnay, É., Dufumier, B., Grigis, A., & Gori, P. (2026) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-026-01233-8

Hardness-Aware Kernelized Contrastive Learning for Brain Age PredictionPreprint

Liu, X., Liu, X., Wang, H., Fu, X., Chen, Y., Elazab, A., Xu, T., Chen, K., Zhang, Y., Qin, F., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-10446726/v1

From Volumetrics to 3D Tensors: A Multi-Cohort Evaluation of Machine Learning and Deep Learning for Alzheimer’s Classification

Kamalov, F., & Ibrahim, A. K. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02240-3

Multimodal Contrastive Regression for Organ-Resolved Biological Age Prediction

Ecker, V., Förster, T., Gatidis, S., Küstner, T., & Yang, B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38260-3_35

Loc-Aware: Integrating Geographic Metadata into Contrastive Learning

Carvalho, J., Rambour, C., Breloy, A., & Castillo-Navarro, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-39385-2_38

Revisiting Model Inversion Evaluation: From Misleading Standards to Reliable Privacy Assessment

Ho, S.-T., Hao, K. J., Nguyen, N.-B., Binder, A., & Cheung, N. (2026) · doi:10.1109/cvprf71909.2026.00775

3D Self-Supervised Methods for Medical ImagingPreprint

Taleb, A., Loetzsch, W., Danz, N., Severin, J., Gaertner, T., Bergner, B., & Lippert, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.03829 · Cited by 139 · see citing works

Multiview Long-Short Spatial Contrastive Learning For 3D Medical Image Analysis

Cao, G., Wang, Y., Zhang, M., Zhang, J., Kang, G., & Xu, X. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747913 · Cited by 4 · see citing works

Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning

Chalcroft, L., Crinion, J., Price, C. J., & Ashburner, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05573-6_7

Embedding Task Knowledge into 3D Neural Networks via Self-supervised LearningPreprint

Zhu, J., Li, Y., Hu, Y., & Zhou, S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.05798 · Cited by 4 · see citing works

Self-Supervised Learning for 3D Medical Image Analysis using 3D SimCLR and Monte Carlo DropoutPreprint

Ali, Y., Taleb, A., Höhne, M., & Lippert, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.14288 · Cited by 9 · see citing works