ArticleBioengineering (Basel, Switzerland)2023
Self-Supervision for Medical Image Classification: State-of-the-Art Performance with ~100 Labeled Training Samples per Class.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed, 17 citations in OpenAlex.
- Surgical Video Understanding with Alignment-Preserving Temporal Adaptation and Action Triplet Text Alignment.Bioengineering (Basel, Switzerland) · 2026Article
- DINO-LG: Enhancing vision transformers with label guidance for coronary artery calcium detection.Medical & biological engineering & computing · 2026Article
- Intelligent identification of medical and veterinary intracellular protozoa by using self-supervised learning.Parasites & vectors · 2026Article
- Medical slice transformer for improved diagnosis and explainability on 3D medical images with DINOv2.Scientific reports · 2025Article
- Cluster-based human-in-the-loop strategy for improving machine learning-based circulating tumor cell detection in liquid biopsy.Patterns (New York, N.Y.) · 2025Article
- Multi-modal representation learning in retinal imaging using self-supervised learning for enhanced clinical predictions.Scientific reports · 2024Article
- Unbiased single-cell morphology with self-supervised vision transformers.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Is self-supervised deep learning (DL) for medical image analysis already a serious alternative to the de facto standard of end-to-end trained supervised DL? We tackle this question for medical image classification, with a particular focus on one of the currently most limiting factor of the field: the (non-)availability of
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.