This repository is mainly dedicated for listing the recent research advancements in the application of Self-Supervised-Learning in medical images computing field. Inspired by awesome-self-supervised-learning
Self-Supervised learning (SSL) is a hybrid learning approach that combines both supervised and unsupervised learning simultaneously. More clearly, SSL is an approach that aims at learning semantically useful features for a certain task by generating supervisory signal from a pool of unlabeled data without the need for human annotation. These representations is then used for subsequent tasks where the amount of labeled data is limited.
Self-Supervised Learning pipelines in computer vision
- Unlabeled medical imaging data is a abundant, but human annotated data is scarce.
- building a large enough human annotated medical imaging datasets is:
- Expensive.
- Time consuming.
- Requires experienced personnel.
- Prone to patients’ privacy preserving issues.
This repository is a continuation of our survey in the field, please read and consider citing it in your work:
@article{shurrab2022self,
title={Self-supervised learning methods and applications in medical imaging analysis: A survey},
author={Shurrab, Saeed and Duwairi, Rehab},
journal={PeerJ Computer Science},
volume={8},
pages={e1045},
year={2022},
publisher={PeerJ Inc.}
}
Please help contribute this list by contacting me or add pull request
Markdown format: height
- Paper Name.
[[pdf]](link)
[[code]](link)
- Author 1, Author 2, and Author 3. *Conference Year*
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A list of recent self-supervised learning papers in medical imaging published since 2017.
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Papers are collected from peer-reviewed journals and high reputed conferences. However, it might have recent papers on arXiv.
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A meta-data is required along with the paper, e.g. category.
- IEEE Access
- IEEE Transaction on Medical Imaging (IEEE-TMI)
- IEEE Transaction on Biomedical Engineering (IEEE-TBME)
- IEEE Journal of Biomedical and Health Informatics (IEEE-JBHI)
- IEEE Transactions on Image Processing (IEEE-TIP)
- Applied Soft Computing (ASC)
- Computer in Biology and Medicine (CBM)
- Computerized Medical Imaging and Graphics (CMIG)
- Medical Image Analysis (MedIA)
- Machine Learning with Applications (MLwA)
- International Journal of Computer Assisted Radiology and Surgery (IJCARS)
- Nature Machine Intelligence (NMI)
- Pattern Recognition
- Expert Systems with Applications (ESA)
- Neurocomputing
- Diagnostics
- Computer Vision and Pattern Recognition (CVPR)
- Proceedings of Machine Learning Research (PMLR)
- International Conference on Machine Learning (ICML)
- IEEE International Symposium on Biomedical Imaging (ISBI)
- International Conference on Learning Representations (ICLR)
- Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)
- Annual Conference on Neural Information Processing Systems (NIPS)
- International Conference on Medical Imaging with Deep Learning (MIDL)
- International Workshop on Deep Learning in Medical Image Analysis (DLMIA)
- International Conference on Information Processing in Medical Imaging (IPMI)
- IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Joint European Conference on Machine Learning and Knowledge Discovery in Databases (JECMLKDD)
- International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
Paper title | Journal/Conference | Category | Paper link | Code link |
---|---|---|---|---|
COVID-19 Infection Segmentation and Severity Assessment Using a Self-Supervised Learning Approach | Diagnostics | Multiple-tasks/Multi-tasking | Link | NA |
Contrastive Learning with Continuous Proxy Meta-data for 3D MRI Classification | MICCAI | Contrastive | Link | pytorch |
How Transferable are Self-supervised Features in Medical Image Classification Tasks? | PMLR | Contrastive | Link | NA |
Towards Better Understanding and Better Generalization of Low-shot Classification in Histology Images with Contrastive Learning | ICLR | Contrastive | Link | pytorch |
Intra- and Inter-Slice Contrastive Learning for Point Supervised OCT Fluid Segmentation | IEEE-TIP | Contrastive | Link | pytorch |
Multimodal image encoding pre-training for diabetic retinopathy grading | CBM | Generative | Link | NA |
Self-supervised Learning for Few-shot Medical Image Segmentation | IEEE-TMI | NA | Link | pytorch |
Self supervised contrastive learning for digital histopathology | MLwA | Contrastive | Link | pytorch |
DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer | MedIA | Contrastive | Link | DLUP, VISSL, pytorch |
Deep Contrastive Learning Based Tissue Clustering for Annotation-free Histopathology Image Analysis | CMIG | Contrastive | Link | NA |
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics | CVPR | Contrastive | Link | pytorch |
Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification | Diagnostics | Contrastive | Link | NA |
Paper title | Journal/Conference | Category | Paper link | Code link |
---|---|---|---|---|
Transferable Visual Words: Exploiting the Semantics of Anatomical Patterns for Self-Supervised Learning | IEEE-TMI | Multiple-tasks/Multi-tasking | Link | tensorflow pytorch |
Towards Fine-grained Visual Representations by Combining Contrastive Learning with Image Reconstruction and Attention-weighted Pooling | ICML | Multiple-tasks/Multi-tasking | Link | tensorflow |
How Transferable are Self-supervised Features in Medical Image Classification Tasks? | PMLR | Contrastive | Link | NA |
Multimodal Self-supervised Learning for Medical Image Analysis | IPMI | Predictive | Link | NA |
Self-supervised multimodal reconstruction pre-training for retinal computer-aided diagnosis | ESA | Generative | Link | NA |
MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation | ArXiv | Contrastive | Link | NA |
COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction | ArXiv | Contrastive | Link | pytorch |
Momentum contrastive learning for few-shot COVID-19 diagnosis from chest CT images | Pattern Recognition | Contrastive | Link | NA |
Big Self-Supervised Models Advance Medical Image Classification | ArXiv | Contrastive | Link | NA |
Self-supervised Multi-task Representation Learning for Sequential Medical Images | JECMLKDD | Multiple-tasks/Multi-tasking | Link | NA |
Self-path: Self-supervision for classification of pathology images with limited annotations | IEEE-TMI | Multiple-tasks/Multi-tasking | Link | NA |
Twin self-supervision based semi-supervised learning (TS-SSL): Retinal anomaly classification in SD-OCT images | Neurocomputing | Multiple-tasks/Multi-tasking | Link | tensorflow |
Rotation-oriented collaborative self-supervised learning for retinal disease diagnosis. | IEEE-TMI | Multiple-tasks/Multi-tasking | Link | tensorflow |
Volumetric white matter tract segmentation with nested self-supervised learning using sequential pretext tasks | MedIA | Multiple-tasks/Multi-tasking | Link | NA |
Paper title | Journal/Conference | Category | Paper link | Code link |
---|---|---|---|---|
Auto-GAN: Self-Supervised Collaborative Learning for Medical Image Synthesis | AAAI | Generative | Link | NA |
Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-supervised Medical Image Segmentation | MICCAI | Predictive | Link | NA |
Rubik’s Cube+: A self-supervised feature learning framework for 3D medical image analysis | MedIA | Predictive | Link | NA |
Self-Supervised Learning Based on Spatial Awareness for Medical Image Analysis | IEEE Access | Predictive | Link | NA |
Self-supervised Skull Reconstruction in Brain CT Images with Decompressive Craniectomy | MICCAI | Generative | Link | pytorch |
Learning the retinal anatomy from scarce annotated data using self-supervised multimodal reconstruction | ASC | Generative | Link | NA |
Multimodal Transfer Learning-based Approaches for Retinal Vascular Segmentation | ArXiv | Generative | Link | NA |
Multi-modal self-supervised pre-training for joint optic disc and cup segmentation in eye fundus images | ICASSP | Generative | Link | NA |
Self-supervised retinal thickness prediction enables deep learning from unlabelled data to boost classification of diabetic retinopathy | NMI | Generative | Link | tensorflow |
Leveraging Self-supervised Denoising for Image Segmentation | ISBI | Generative | Link | tensorflow |
Self-Supervised Pretraining with DICOM metadata in Ultrasound Imaging | PMLR | Generative | Link | NA |
Revisiting rubik’s cube: Self-supervised learning with volume-wise transformation for 3d medical image segmentation | MICCAI | Generative | Link | NA |
Semi-supervised breast cancer histology classification using deep multiple instance learning and contrast predictive coding | ArXiv | Contrastive | Link | NA |
Embedding Task Knowledge into 3D Neural Networks via Self-supervised Learning | ArXiv | Contrastive | Link | NA |
PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation | ArXiv | Contrastive | Link | pytorch |
Self-Supervised Feature Learning via Exploiting Multi-Modal Data for Retinal Disease Diagnosis | IEEE-TMI | Contrastive | Link | pytorch |
MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models | PMLR | Contrastive | Link | pytorch |
Contrastive learning of global and local features for medical image segmentation with limited annotations | ArXiv | Contrastive | Link | tensorflow |
Self-Supervised Representation Learning for Ultrasound Video | ISBI | Multiple-tasks/Multi-tasking | Link | NA |
A Multi-Task Self-Supervised Learning Framework for Scopy Images | ISBI | Multiple-tasks/Multi-tasking | Link | NA |
3D Self-Supervised Methods for Medical Imaging--update references | NIPS | Multiple-tasks/Multi-tasking | Link | tensorflow |
Retinal Image Classification by Self-Supervised Fuzzy Clustering Network | IEEE Access | Multiple-tasks/Multi-tasking | Link | NA |
Learning semantics-enriched representation via self-discovery, self-classification, and self-restoration | MICCAI | Multiple-tasks/Multi-tasking | Link | pytorch |
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation | ArXiv | Multiple-tasks/Multi-tasking | Link | NA |
Paper title | Journal/Conference | Category | Paper link | Code link |
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Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction | MICCAI | Predictive | Link | NA |
Self-supervised Feature Learning for 3D Medical Images by Playing a Rubik’s Cube | MICCAI | Predictive | Link | NA |
Self-supervised learning for medical image analysis using image context restoration | MedIA | Generative | Link | NA |
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis | MICCAI | Generative | Link | tensorflow & pytorch |
Surrogate Supervision for Medical Image Analysis: Effective Deep Learning From Limited Quantities of Labeled Data | ISBI | Multiple-tasks/Multi-tasking | Link | NA |
Paper title | Journal/Conference | Category | Paper link | Code link |
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Exploiting the potential of unlabeled endoscopic video data with self-supervised learning | IJCARS | Generative | Link | NA |
Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks | MICCAI | Predictive | Link | NA |
Paper title | Journal/Conference | Category | Paper link | Code link |
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Self-supervised Learning for Spinal MRIs | DLMIA | Contrastive | Link | NA |
Self supervised deep representation learning for fine-grained body part recognition | ISBI | Predictive | Link | NA |