Rotation Equivariant CNNs for Digital Pathology
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For Camelyon17-wilds, because the dataset was balanced so that each split has a 50/50 class balance, average accuracy is a reasonable measure of performance.
“To improve accessibility, we pre-process the slides into patches and balance the dataset so that each split has a 50/50 class balance, making average accuracy is a reasonable measure of performance (Veeling et al., 2018; Tellez et al., 2019).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Veeling et al., 2018
Loki Annotate was applied to four benchmark histopathology datasets including CRC7K (eight tissue types), WSSS-4LUAD (normal and tumor), PatchCamelyon (normal and tumor) and LC25000 (benign and malignant).
“We applied Loki Annotate to four benchmark histopathology datasets including CRC7K41 (eight tissue types), WSSS-4LUAD42 (normal and tumor), PatchCamelyon43 (normal and tumor) and LC2500044 (benign and malignant).”
From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [43]
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References 19 · Cited by 476
- Authors
- Veeling, B. S., Linmans, J., Winkens, J., Cohen, T., & Welling, M.
- Affiliations
- University of Amsterdam (NL)
- Corresponding author
- Veeling, Bastiaan S.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 210-218
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2806857275
- Cited by
- 476
- References
- 19
- Citations by year
- 2026: 39 · 2025: 65 · 2024: 85 · 2023: 66 · 2022: 71 · 2021: 67 · 2020: 56 · 2019: 23 · 2018: 4
- FWCI
- 15.41
- Type
- conference paper
- Language
- en
- Open access
- green
- zenodo.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- digital pathology, histopathology image segmentation, tumor detection, lymph node metastasis, image symmetry, reflection symmetry, benchmark dataset, histopathological diagnosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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