Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the�…
of clinical experts. However, in settings differing from those of the training dataset, the�…
Robust and efficient medical imaging with self-supervision
Recent progress in Medical Artificial Intelligence (AI) has delivered systems that can reach
clinical expert level performance. However, such systems tend to demonstrate sub-optimal"�…
clinical expert level performance. However, such systems tend to demonstrate sub-optimal"�…
Contrastive self-supervised learning from 100 million medical images with optional supervision
Purpose Building accurate and robust artificial intelligence systems for medical image
assessment requires the creation of large sets of annotated training examples. However�…
assessment requires the creation of large sets of annotated training examples. However�…
[HTML][HTML] Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging
D Wolf, T Payer, CS Lisson, CG Lisson, M Beer…�- Scientific Reports, 2023 - nature.com
Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors,
reduce radiologist workload, and accelerate diagnosis. Training such deep learning models�…
reduce radiologist workload, and accelerate diagnosis. Training such deep learning models�…
How transferable are self-supervised features in medical image classification tasks?
T Truong, S Mohammadi…�- Machine Learning for�…, 2021 - proceedings.mlr.press
Transfer learning has become a standard practice to mitigate the lack of labeled data in
medical classification tasks. Whereas finetuning a downstream task using supervised�…
medical classification tasks. Whereas finetuning a downstream task using supervised�…
[HTML][HTML] Self-supervised learning methods and applications in medical imaging analysis: A survey
The scarcity of high-quality annotated medical imaging datasets is a major problem that
collides with machine learning applications in the field of medical imaging analysis and�…
collides with machine learning applications in the field of medical imaging analysis and�…
[HTML][HTML] Self-supervised learning for medical image classification: a systematic review and implementation guidelines
Advancements in deep learning and computer vision provide promising solutions for
medical image analysis, potentially improving healthcare and patient outcomes. However�…
medical image analysis, potentially improving healthcare and patient outcomes. However�…
Preservational learning improves self-supervised medical image models by reconstructing diverse contexts
Preserving maximal information is the basic principle of designing self-supervised learning
methodologies. To reach this goal, contrastive learning adopts an implicit way which is�…
methodologies. To reach this goal, contrastive learning adopts an implicit way which is�…
Self-supervised learning in medicine and healthcare
The development of medical applications of machine learning has required manual
annotation of data, often by medical experts. Yet, the availability of large-scale unannotated�…
annotation of data, often by medical experts. Yet, the availability of large-scale unannotated�…
Multimodal self-supervised learning for medical image analysis
Self-supervised learning approaches leverage unlabeled samples to acquire generic
knowledge about different concepts, hence allowing for annotation-efficient downstream�…
knowledge about different concepts, hence allowing for annotation-efficient downstream�…