Semi-supervised Medical Image Classification

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

7 shown of 7 papers with code (13 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
  15 Jan 2021 2 repositories listed Syntology ran 0 of 5 samples · 5 unverified · 3 pointer-only (licence)
  The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance.
• Universal Semi-Supervised Learning for Medical Image Classification
  8 Apr 2023 1 repository listed
  However, in practical scenarios, unlabeled data would be from unseen classes or unseen domains, and it is still challenging to exploit them by existing SSL methods.
• ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
  25 Nov 2021 1 repository listed
  Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two challenges: 1) work effectively on both multi-class (e.
• Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching
  16 Jun 2021 1 repository listed
  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…
• Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification
  5 Mar 2021 1 repository listed
  In this paper, we propose Self-supervised Mean Teacher for Semi-supervised (S²MTS²) learning that combines self-supervised mean-teacher pre-training with semi-supervised fine-tuning.
• Semi-supervised Medical Image Classification with Global Latent Mixing
  22 May 2020 1 repository listed
  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.
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model
  15 May 2020 1 repository listed
  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…

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.