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

Shih-Cheng Huang, Anuj Pareek, Malte Jensen, Matthew P Lungren, Serena Yeung, Akshay S Chaudhari, Shih-Cheng Huang, Anuj Pareek, Malte Jensen, Matthew P Lungren, Serena Yeung, Akshay S Chaudhari

Abstract

Advancements in deep learning and computer vision provide promising solutions for medical image analysis, potentially improving healthcare and patient outcomes. However, the prevailing paradigm of training deep learning models requires large quantities of labeled training data, which is both time-consuming and cost-prohibitive to curate for medical images. Self-supervised learning has the potential to make significant contributions to the development of robust medical imaging models through its ability to learn useful insights from copious medical datasets without labels. In this review, we provide consistent descriptions of different self-supervised learning strategies and compose a systematic review of papers published between 2012 and 2022 on PubMed, Scopus, and ArXiv that applied self-supervised learning to medical imaging classification. We screened a total of 412 relevant studies and included 79 papers for data extraction and analysis. With this comprehensive effort, we synthesize the collective knowledge of prior work and provide implementation guidelines for future researchers interested in applying self-supervised learning to their development of medical imaging classification models.

Conflict of interest statement

The authors declare no competing interests.

© 2023. The Author(s).

Figures

Fig. 1. Timeline showing the number of…
Fig. 1. Timeline showing the number of publications on deep learning for medical image classification per year, found by using the same search criteria on PubMed, Scopus and, ArXiv.
The figure shows that self-supervised learning is a rapidly growing subset of deep learning for medical imaging literature.
Fig. 2. Illustration of different self-supervised learning…
Fig. 2. Illustration of different self-supervised learning and fine-tuning strategies.
During Stage 1 a model is pre-trained using one or more of the following self-supervised learning strategies: (a) Innate relationship SSL pre-trains a model on a hand-crafted task by leveraging the internal structure of the data, (b) Generative SSL learns the distribution of training data, enabling reconstruction of the original input (c) Contrastive SSL forms positive pairs between different augmentations of the same image and minimizes representational distances of positive samples (d) Self-prediction augments or masks out random portions of an image, and reconstructs the original image based on the unaltered parts of the original image. During Stage 2, the pre-trained model can be fine-tuned using one of the following strategies: (e) end-to-end fine-tuning of the pre-trained model and classifier, or (f) train a classifier that uses extracted features from the SSL pre-trained model.
Fig. 3. The PRISMA diagram for this…
Fig. 3. The PRISMA diagram for this review.
The authors independently screened all records for eligibility. Out of 412 studies identified from PubMed, Scopus, and ArXiv, 79 studies were included in the systematic review.
Fig. 4. Summary of extracted data from…
Fig. 4. Summary of extracted data from studies in our system review.
a Prevalence of different medical specialties split by self-supervised learning strategy. b Prevalence of different medical imaging modalities split by self-supervised learning strategy. c Relative performance difference between different types of self-supervised learning strategies on the same task. d Performance comparison between end-to-end fine-tuning vs. training a classifier using extracted features from pre-trained self-supervised models. e Relative difference in downstream task performance between self-supervised and non-self-supervised models.
Fig. 5. Examples of augmentations and transformations…
Fig. 5. Examples of augmentations and transformations that alter the semantic meaning of medical images but not natural images.
a The image shows a T2-weighted brain MRI with a cavernoma in the right parietal lobe (bounded in red). b and c Masking and cropping operations can obscure the cavernoma and alter the semantic meaning of the image, as the MRI-scan no longer exhibits any abnormality. d Image of a dog (bounded in red), (e) and (f) Masking and cropping operations do not obscure the dog nor alter the semantic meaning of the image.

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