Breast Tumour Classification

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

4 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.

Most implemented papers archive 2025-07-28

10 shown of 10 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.

• Densely Connected Convolutional Networks
  25 Aug 2016 146 repositories listed Syntology ran 18 of 71 samples · 53 unverified · 7 pointer-only (licence)
  Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.
• BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
  7 Apr 2020 8 repositories listed
  This paper describes the field research, design and comparative deployment of a multimodal medical imaging user interface for breast screening.
• Rotation Equivariant CNNs for Digital Pathology
  8 Jun 2018 4 repositories listed
  We propose a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection.
• Rotation equivariant vector field networks
  29 Dec 2016 3 repositories listed
  In many computer vision tasks, we expect a particular behavior of the output with respect to rotations of the input image.
• Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
  6 Apr 2020 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear.
• Virchow: A Million-Slide Digital Pathology Foundation Model
  14 Sep 2023 1 repository listed Syntology ran 9 of 9 samples · 0 unverified · 9 pointer-only (licence)
  The use of artificial intelligence to enable precision medicine and decision support systems through the analysis of pathology images has the potential to revolutionize the diagnosis and treatment of cancer.
• Multi-View Hypercomplex Learning for Breast Cancer Screening
  12 Apr 2022 1 repository listed Syntology ran 3 of 7 samples · 4 unverified
  To overcome such limitations, in this paper, we propose a methodological approach for multi-view breast cancer classification based on parameterized hypercomplex neural networks.
• Meta-repository of screening mammography classifiers
  10 Aug 2021 1 repository listed
  Artificial intelligence (AI) is showing promise in improving clinical diagnosis.
• Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
  20 Feb 2020 1 repository listed Syntology ran 0 of 6 samples · 6 unverified
  This study is focused on histopathology image analysis applications for which it is desirable that the arbitrary global orientation information of the imaged tissues is not captured by the machine learning models.
• Group Equivariant Convolutional Networks
  24 Feb 2016 1 repository listed
  We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries.

Syntology lines on 5 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.