Semi-supervised Medical Image Classification
6 papers with code • 1 benchmarks • 1 datasets
Semi-supervised Medical Image Classification
Benchmarks
These leaderboards are used to track progress in Semi-supervised Medical Image Classification
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OLIVES Dataset
Most implemented papers
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance.
Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model
liuquande/SRC-MT
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It is a consistency-based method which exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations, and leverages a self-ensembling model to produce high-quality consistency targets for the unlabeled data.
Semi-supervised Medical Image Classification with Global Latent Mixing
In this work, we argue that regularizing the global smoothness of neural functions by filling the void in between data points can further improve SSL.
Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification
In this paper, we propose Self-supervised Mean Teacher for Semi-supervised (S$^2$MTS$^2$) learning that combines self-supervised mean-teacher pre-training with semi-supervised fine-tuning.
Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching
liuquande/FedIRM
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This paper studies a practical yet challenging FL problem, named \textit{Federated Semi-supervised Learning} (FSSL), which aims to learn a federated model by jointly utilizing the data from both labeled and unlabeled clients (i. e., hospitals).
ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
FBLADL/ACPL
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CVPR 2022
Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two challenges: 1) work effectively on both multi-class (e. g., lesion classification) and multi-label (e. g., multiple-disease diagnosis) problems, and 2) handle imbalanced learning (because of the high variance in disease prevalence).
