Pith. sign in

REVIEW 6 cited by

Rotation Equivariant CNNs for Digital Pathology

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1806.03962 v1 pith:QAJL3UWU submitted 2018-06-08 cs.CV cs.LGstat.ML

Bastiaan S. Veeling , Jasper Linmans , Jim Winkens , Taco Cohen , Max Welling This is my paper · ORCID
classification cs.CVcs.LGstat.ML
keywords rotationdatasetbenchmarkchallengingcnnsdigitalequivarianthistopathology
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

We propose a new model for digital pathology segmentation, based on the observation that histopathology images are inherently symmetric under rotation and reflection. Utilizing recent findings on rotation equivariant CNNs, the proposed model leverages these symmetries in a principled manner. We present a visual analysis showing improved stability on predictions, and demonstrate that exploiting rotation equivariance significantly improves tumor detection performance on a challenging lymph node metastases dataset. We further present a novel derived dataset to enable principled comparison of machine learning models, in combination with an initial benchmark. Through this dataset, the task of histopathology diagnosis becomes accessible as a challenging benchmark for fundamental machine learning research.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Unified Framework for Vision Transformers Equivariant to Discrete Subgroups of $\mathrm{O}(2)$

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    Constructs G-equivariant ViTs for arbitrary discrete G ≤ O(2), proves H ≤ G implies G-models embed into H-models and single-head equivariant attention realizes all ordinary G-equivariant maps, introduces D6 hexagonal ...

  2. SAFE-Merge: Data-Free Continual Model Merging with General Knowledge Preservation

    cs.LG 2026-08 conditional novelty 6.0 of 10

    SAFE-Merge masks risk-prone parameter updates and recovers lost task information with a constrained low-rank correction, achieving the best H-score in data-free continual model merging benchmarks.

  3. Towards Higher Effective Rank in Parameter-efficient Fine-tuning using Khatri--Rao Product

    cs.LG 2025-08 conditional novelty 6.0 of 10

    KRAdapter, a Khatri-Rao product adapter, produces full-rank high-effective-rank weight updates for parameter-efficient fine-tuning and reports improved out-of-distribution performance over LoRA and other full-rank PEF...

  4. Beyond Backbone Backpropagation: A Decoupled Strategy for Efficient Transfer Learning

    cs.LG 2026-06 conditional novelty 5.0 of 10

    A decoupled transfer-learning method that freezes the backbone, retunes only normalization statistics, and reweights ambiguous samples matches standard last-layer fine-tuning on most medical benchmarks at 10-20x lower...

  5. Visual Instance-aware Prompt Tuning

    cs.CV 2025-07 conditional novelty 5.0 of 10

    ViaPT generates instance-aware prompts per image, fuses them with dataset-level prompts, and applies PCA compression to outperform VPT-Deep and other PEFT baselines on FGVC, HTA, and VTAB-1k.

  6. Symmetry-preserving neural networks in lattice field theories

    hep-lat 2025-06 conditional novelty 4.0 of 10

    Translation- and gauge-equivariant neural networks (L-CNNs) predict Wilson loops, topological charge, and flux observables with orders-of-magnitude lower error than symmetry-breaking baselines, and neural gradient flo...

Pith tools