Pith. sign in

REVIEW 4 major objections 3 minor 4 cited by

Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit\'e, and Aignostics

T0 review · 4 major / 3 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Atlas, a 632-million-parameter pathology model, tops 21 public benchmarks with a 61.9% average.

desk verdict Solid, transparent model report whose SOTA claim is plausible but not statistically pinned down — worth reviewing, but the 1.1 pp headline margin shrinks to 0.4 pp under CLS-only readout. read the letter →

arxiv 2501.05409 v2 pith:CW554276 submitted 2025-01-09 cs.CV cs.AIcs.LG

Maximilian Alber , Stephan Tietz , Jonas Dippel , Timo Milbich , Timothée Lesort , Panos Korfiatis , Moritz Krügener , Beatriz Perez Cancer
show 19 more authors
Neelay Shah Alexander Möllers Philipp Seegerer Alexandra Carpen-Amarie Kai Standvoss Gabriel Dernbach Edwin de Jong Simon Schallenberg Andreas Kunft Helmut Hoffer von Ankershoffen Gavin Schaeferle Patrick Duffy Matt Redlon Philipp Jurmeister David Horst Lukas Ruff Klaus-Robert Müller Frederick Klauschen Andrew Norgan
This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords pathologyfoundationmodelself-supervisedlearningDINOv2ViT-H/14wholeslideimageslinearprobinggeneexpressionpredictiondigital
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces Atlas, a ViT-H/14 pathology tile encoder with 632 million parameters, trained on 1.2 million whole-slide images (about 3.4 billion tiles) from two hospital systems using the RudolfV self-supervised recipe built on DINOv2. The authors' central claim is that Atlas sets a new state of the art on a 21-dataset public benchmark suite, averaging 61.9% across molecular (gene-expression prediction) and morphology (tissue and cell classification) tasks, which is 1.1 percentage points above the two closest contenders, Virchow2 and H-Optimus-0. The result matters because Atlas is neither the largest model nor the one trained on the most slides, suggesting that training-data diversity (multiple stains, magnifications, and institutions) can compensate for raw scale. If the claim holds, it shifts attention from simply scaling parameters and data volume toward curation and sampling strategy for pathology foundation models.

What carries the argument

The engine of the result is Atlas itself: a ViT-H/14 vision transformer with 632 million parameters, trained with the RudolfV paradigm, a DINOv2-based self-supervised approach, on roughly 520 million sampled tiles drawn from 1.2 million whole-slide images covering more than 70 tissue types, over 100 staining types, and seven scanners across two institutions. The training tiles are extracted at four magnifications (0.25, 0.5, 1.0, and 2.0 microns per pixel), and the corpus includes H&E, immunohistochemistry, and special stains. The evaluation machinery is the linear-probing protocol: for each frozen backbone, both the CLS token and the CLS+Mean token (CLS concatenated with the mean of patch tokens) are probed and the better score is kept, with tasks run under the eva and HEST frameworks plus an internal logistic-regression pipeline.

What would settle it

Re-run the full 21-benchmark evaluation under a single fixed token rule (CLS only, or CLS+Mean only) and compute a paired, seed-level significance test across tasks; if the Atlas advantage over Virchow2 and H-Optimus-0 shrinks to within one standard error or flips sign on several tasks, the state-of-the-art claim would fail. A simpler check: count how often Atlas wins when differences are required to exceed the reported per-task standard deviation.

Watch

Extended reading notes

Core claim

On its own terms, the paper claims that Atlas achieves state-of-the-art performance across twenty-one public benchmark datasets, with an average score of 61.9%, a 1.1 percentage point improvement over the two closest contenders, Virchow2 and H-Optimus-0, both at 60.8%. Atlas leads on 11 of the 21 tasks, is second-best on 7 of the remaining 10, and is below average on a single benchmark, TCGA Uniform (20×). The margin is concentrated in morphology tasks, where Atlas averages 84.6% versus Virchow2's 84.0%, while on molecular tasks Atlas (44.9%) and H-Optimus-0 (44.8%) are effectively tied. The evaluation uses frozen-backbone linear probing, reporting the better of CLS-token and CLS+Mean-token embeddings for every model, with five seeds per task.

Load-bearing premise

The headline claim depends on the 1.1-percentage-point average edge over Virchow2 and H-Optimus-0 being a real effect rather than evaluation noise, since per-task standard deviations across the five seeds are often several times larger than that margin.

Editorial extensions

If this is right

  • If the claim holds, pathology foundation models do not need to maximize parameters or slide count; a mid-sized model trained on a diverse mid-sized corpus can outscore both a larger-parameter model and a larger-data model on average.
  • Multi-stain, multi-magnification training transfers to both morphology and molecular tasks, making Atlas a strong single encoder for TIL detection, cancer typing, and gene-expression prediction.
  • The 21-task average of 61.9% becomes a reference number that future pathology foundation-model papers will need to beat, and the per-task table provides a granular checklist for where gains come from.
  • Clinically oriented downstream applications, such as MSI detection and Gleason grading, inherit whatever gains the encoder provides, since those tasks are among the 21 benchmarks.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the reported margin is smaller than per-task standard deviations, a seed-paired significance test could show that Atlas and its closest rivals are statistically indistinguishable; that test is not in the paper.
  • Atlas's one clear failure, TCGA Uniform (20×), hints at magnification-specific sensitivity; a testable extension is training or fine-tuning with magnification-conditioned tokens.
  • The two-institution corpus raises an open generalization question: whether the advantage persists on slides from scanners and staining protocols outside Mayo Clinic and Charité.
  • The better-of-CLS/CLS+Mean rule inflates every model's score, but it may not inflate them equally; reporting the chosen token per task would make leaderboards more reproducible.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 3 minor

Summary. This report introduces Atlas, a 632M-parameter ViT-H/14 tile encoder for histopathology, trained on 1.2 million whole-slide images from Mayo Clinic and Charité using the RudolfV/DINOv2 paradigm. The authors evaluate Atlas against six public pathology foundation models on 21 public benchmarks spanning gene-expression prediction, MSI classification, TIL detection, cancer subtyping, tissue classification, and slide-level tasks, using the eva and HEST evaluation frameworks plus an internal linear-probing framework. The paper's central claim is that Atlas achieves a 61.9% average score, a 1.1 percentage-point improvement over Virchow2 and H-Optimus-0, despite being neither the largest model by parameter count nor the model trained on the most data.

Significance. If established, the result would be a practically useful reference point for pathology foundation models: it suggests that broad real-world data diversity (multiple stains, magnifications, scanners) can yield a strong tile encoder without maximal model or data scale. The evaluation has real strengths: twenty-one public benchmarks, external evaluation frameworks for most tasks, five-seed or five-fold repetitions with reported variation, direct comparison with six leading models, and a token-readout ablation in the appendix. The circularity risk is low because the headline numbers are measured on benchmarks not constructed by the authors. However, the headline margin is fragile and currently not statistically substantiated, so the main contribution is conditional on additional analysis.

major comments (4)
  1. [§4 and Table 1; Table 3] The central claim that Atlas beats Virchow2 and H-Optimus-0 by 1.1 p.p. on average is not backed by a significance test. The per-task uncertainties in Table 3 are much larger than the aggregate margin: for example, HEST-IDC is 60.4±8.3 for Atlas versus 61.0±8.1 for H-Optimus-0, HEST-PAAD is 51.8±7.4 versus 50.9±4.3, and HEST-SKCM is 62.5±2.4 versus 66.1±5.8, the latter favoring H-Optimus-0 beyond the headline gap. Section 3.3 calls the reported values "standard errors" while Table 3's caption calls them "standard deviation"; either way, no confidence interval for the 21-task average difference is given. A paired bootstrap or Wilcoxon signed-rank test across the 21 tasks is needed before "state-of-the-art" is claimed.
  2. [§3.3, Table 4] The evaluation rule "report the better (maximum) performance of the two" for CLS and CLS+Mean tokens is load-bearing for the ranking. With CLS-only embeddings, Atlas averages 60.6% over all 21 tasks, only 0.4 p.p. above Virchow2 (60.2%), whereas with the max rule the lead is 1.1 p.p. and under CLS+Mean Virchow2 averages 60.8%, close to Atlas's 61.9%. Thus the magnitude of the advantage is sensitive to the readout-selection rule, and applying the max per task can inflate each model's score differently. The authors should either prespecify a single readout before seeing results or provide a paired comparison that treats the readout rule as part of the protocol and reports both the per-task selection and a fixed-readout sensitivity analysis.
  3. [§3.3, §4, Tables 1–3] The overall "average performance score" mixes Pearson correlations from the ten HEST regression tasks with balanced accuracies from the eleven classification tasks. These metrics are on different scales, and the unweighted average gives equal weight to high-variance tasks such as HEST-IDC and HEST-PAAD alongside near-deterministic tasks such as TCGA Uniform and CRC-100k. As a result, "61.9%" is not a directly interpretable performance measure, and the 1.1 p.p. difference could be driven by the choice of task weighting rather than by a consistent model advantage. The authors should report a paired difference analysis on a common scale (for example, rank-normalizing each task) or at least show the per-group averages with confidence intervals.
  4. [Appendix A.2, Table 3] Several entries in Table 3 report zero standard deviation for tasks where fold-to-fold variation is expected, including HEST-COAD for Gigapath (30.7±0.0), HEST-PRAD for H-Optimus (38.5±0.0), and all TCGA Uniform rows, whereas other HEST rows exceed ±7 p.p. The caption says these are standard deviations over five data splits, but the zeros suggest either deterministic splits, a different number of runs, or a reporting artifact. The paper should clarify this so that the uncertainty information can actually be used to assess whether Atlas's edge is real.
minor comments (3)
  1. [§3.3 and Table 3 caption] The text says "We report the mean performance and standard errors over the seeds" while Table 3's caption says "standard deviation over the 5 data splits"; please align the terminology and state explicitly whether the reported ± values are standard errors, standard deviations, or something else.
  2. [Abstract and Section 4] The phrase "state-of-the-art" in the abstract and in Section 4 should be qualified until the significance analysis requested in my major comments is provided, since the point estimate alone does not establish superiority over the closest contenders.
  3. [General] The paper does not include a model or code availability statement; for a foundation model report, an explicit statement on whether the Atlas weights and evaluation code will be released would greatly aid reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the SOTA claim is measured on external public benchmarks, not derived from the model's own training objective or fitted parameters.

full rationale

The paper's central claim is an empirical evaluation result: Atlas is trained with a self-cited method (RudolfV [10]) and then compared on 21 public benchmarks using external frameworks (eva, HEST) and public datasets. The performance numbers in Table 1 are measurements from frozen-backbone linear probing, ABMIL, and regression protocols, not quantities defined in terms of Atlas's training data or fitted to the benchmarks. The choice to report max(CLS, CLS+Mean) per model is an evaluation protocol applied symmetrically to all models; it may affect the magnitude of the SOTA margin, but it is not a fitted input that makes the prediction equal to the input by construction. The self-citation to RudolfV describes the training paradigm, not the benchmark outcome, so it is not load-bearing for the SOTA claim. Statistical concerns about the 1.1 p.p. margin (per-task standard deviations and absence of a paired confidence interval) are validity concerns, not circularity. Therefore no circular step can be exhibited with a specific reduction.

Assumptions & free parameters 0 free parameters · 4 assumptions · 1 invented entities

No free parameters are fitted to benchmarks in the derivation sense; the L2 regularization in the internal LP framework is a standard cross-validated hyperparameter. The central claim rests on domain assumptions about the transferability of the self-cited RudolfV training paradigm, the validity and statistical sensitivity of the 21-benchmark evaluation, the fairness of the max-of-two-token readout, and the representativeness of the private training corpus.

assumptions (4)
  • domain assumption The RudolfV sampling and DINOv2-based self-supervised training paradigm produces high-quality pathology embeddings when scaled to 1.2M slides.
    The paper adapts the authors' prior RudolfV approach ([10]) without re-validating that its sampling and training choices transfer to the larger, multi-institutional, multi-stain dataset.
  • domain assumption The 21 public benchmark datasets, with the eva and HEST evaluation protocols, constitute a valid and sufficiently sensitive measure of foundation model quality.
    The SOTA claim rests on the average over these benchmarks; per-task standard deviations are large relative to the 1.1pp margin.
  • ad hoc to paper For each model, the better of CLS and CLS+Mean token embeddings provides a fair comparison without systematically favoring any model.
    The max-token selection is introduced in Section 3.3 and could inflate scores; it is applied uniformly, but its fairness is assumed.
  • domain assumption The de-identified 1.2M-slide corpus from Mayo Clinic and Charité is representative and free of label or preprocessing bias that would affect downstream evaluation.
    Training data is private; the report does not audit for selection biases across institutions, scanners, or stains.
invented entities (1)
  • Atlas foundation model (ViT-H/14)
    purpose: To provide a general-purpose pathology tile encoder trained on 1.2M multi-stain, multi-magnification slides.
    The model weights are not released, and the training data is private; the only evidence of its properties is the benchmark table in this paper. If weights were released, the benchmark results could be independently verified.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit\'e, and Aignostics." pith.science (2026). https://pith.science/paper/CW554276

@misc{pith2026250105409,
  author       = {Pith},
  title        = {Pith review of: Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charit\'e, and Aignostics},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CW554276}},
  note         = {Machine review of arXiv:2501.05409}
}
read the original abstract

Recent advances in digital pathology have demonstrated the effectiveness of foundation models across diverse applications. In this report, we present Atlas, a novel vision foundation model based on the RudolfV approach. Our model was trained on a dataset comprising 1.2 million histopathology whole slide images, collected from two medical institutions: Mayo Clinic and Charit\'e - Universt\"atsmedizin Berlin. Comprehensive evaluations show that Atlas achieves state-of-the-art performance across twenty-one public benchmark datasets, even though it is neither the largest model by parameter count nor by training dataset size.

Figures

Figures reproduced from arXiv: 2501.05409 by the authors.

Figure 1
Figure 1. Overview of average performance, training dataset size, and model size of different contenders. The [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. (A) shows the key training dataset statistics. The dataset was derived from 1.2 million pathology slides [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

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

  1. Atlas H&E-TME: Scalable AI-Based Tissue Profiling at Expert Pathologist-Level Accuracy

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    Atlas H&E-TME is a new AI system for cell-level tissue profiling on H&E slides that matches pathologist performance when validated against an IHC-informed consensus and a large multi-cancer H&E annotation set.

  2. Towards Robust Foundation Models for Digital Pathology

    eess.IV 2025-07 conditional novelty 6.0 of 10

    PathoROB shows that all 20 evaluated pathology foundation models encode medical center information and that lower robustness correlates with larger downstream performance drops.

  3. MeDi: Metadata-Guided Diffusion Models for Mitigating Biases in Tumor Classification

    eess.IV 2025-06 conditional novelty 6.0 of 10

    Conditioning a histopathology diffusion model on tissue source site metadata improves synthetic image fidelity and enhances downstream tumor classification under subpopulation shift.

  4. A Survey on Computational Pathology Foundation Models: Datasets, Adaptation Strategies, and Evaluation Tasks

    cs.CV 2025-01 conditional novelty 4.0 of 10

    A systematic literature review of computational pathology foundation models that catalogs datasets, SSL adaptation strategies, and evaluation tasks into taxonomies.

Reference graph

Works this paper leans on

42 extracted references · 29 canonical work pages · cited by 4 Pith papers

  1. [1]

    de Jong, Ioannis Gatopoulos, Nicolas Känzig, Mikhail Karasikov, Axel Lagré, Roman Moser, Joost van Doorn, and Fei Tang

    Nanne Aben, Edwin D. de Jong, Ioannis Gatopoulos, Nicolas Känzig, Mikhail Karasikov, Axel Lagré, Roman Moser, Joost van Doorn, and Fei Tang. Towards Large-Scale Training of Pathology Foundation Models, March 2024. arXiv:2404.15217

    arXiv 2024

  2. [2]

    Deep learning-based mapping of tumor infiltrating lymphocytes in whole slide images of 23 types of cancer

    Shahira Abousamra, Rajarsi Gupta, Le Hou, Rebecca Batiste, Tianhao Zhao, Anand Shankar, Arvind Rao, Chao Chen, Dimitris Samaras, Tahsin Kurc, et al. Deep learning-based mapping of tumor infiltrating lymphocytes in whole slide images of 23 types of cancer. Frontiers in oncology, 11:806603, 2022

    work page 2022

  3. [3]

    BACH: Grand challenge on breast cancer histology images

    Guilherme Aresta, Teresa Araújo, Scotty Kwok, Sai Saketh Chennamsetty, Mohammed Safwan, Varghese Alex, Bahram Marami, Marcel Prastawa, Monica Chan, Michael Donovan, et al. BACH: Grand challenge on breast cancer histology images. Medical image analysis, 56:122– 139, 2019

    work page 2019

  4. [4]

    Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

    Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes Van Diest, Bram Van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen AWM Van Der Laak, Meyke Hermsen, Quirine F Manson, Maschenka Balkenhol, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA, 318(22):2199–2210, 2017

    2017

  5. [5]

    Morphological and molecular breast cancer profiling through explainable machine learning

    Alexander Binder, Michael Bockmayr, Miriam Hägele, Stephan Wienert, Daniel Heim, Katha- rina Hellweg, Masaru Ishii, Albrecht Stenzinger, Andreas Hocke, Carsten Denkert, et al. Morphological and molecular breast cancer profiling through explainable machine learning. Nature Machine Intelligence, 3(4):355–366, 2021

    work page 2021

  6. [6]

    Artificial intelligence for diagnosis and gleason grading of prostate cancer: the PANDA challenge.Nature medicine, 28(1):154–163, 2022

    Wouter Bulten, Kimmo Kartasalo, Po-Hsuan Cameron Chen, Peter Ström, Hans Pinckaers, Kunal Nagpal, Yuannan Cai, David F Steiner, Hester Van Boven, Robert Vink, et al. Artificial intelligence for diagnosis and gleason grading of prostate cancer: the PANDA challenge.Nature medicine, 28(1):154–163, 2022

    work page 2022

  7. [7]

    Chen, Tong Ding, Ming Y

    Richard J. Chen, Tong Ding, Ming Y . Lu, Drew F. K. Williamson, Guillaume Jaume, Andrew H. Song, Bowen Chen, Andrew Zhang, Daniel Shao, Muhammad Shaban, Mane Williams, Lukas Oldenburg, Luca L. Weishaupt, Judy J. Wang, Anurag Vaidya, Long Phi Le, Georg Gerber, Sharifa Sahai, Walt Williams, and Faisal Mahmood. Towards a general-purpose foundation model for ...

    work page 2024

  8. [8]

    Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes

    James A Diao, Jason K Wang, Wan Fung Chui, Victoria Mountain, Sai Chowdary Gullapally, Ramprakash Srinivasan, Richard N Mitchell, Benjamin Glass, Sara Hoffman, Sudha K Rao, et al. Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes. Nature communications, 12(1):1613, 2021

    work page 2021

Show all 42 references
  1. [9]

    Wagner, Andrew H

    Tong Ding, Sophia J. Wagner, Andrew H. Song, Richard J. Chen, Ming Y . Lu, Andrew Zhang, Anurag J. Vaidya, Guillaume Jaume, Muhammad Shaban, Ahrong Kim, Drew F. K. Williamson, Bowen Chen, Cristina Almagro-Perez, Paul Doucet, Sharifa Sahai, Chengkuan Chen, Daisuke Komura, Akihi...

    2024 arXiv

  2. [10]

    RudolfV: A Foundation Model by Pathologists for Pathologists, June 2024

    Jonas Dippel, Barbara Feulner, Tobias Winterhoff, Timo Milbich, Stephan Tietz, Simon Schal- lenberg, Gabriel Dernbach, Andreas Kunft, Simon Heinke, Marie-Lisa Eich, Julika Ribbat-Idel, Rosemarie Krupar, Philipp Anders, Niklas Prenißl, Philipp Jurmeister, David Horst, Lukas Ruf...

    2024 arXiv

  3. [11]

    Ai-based anomaly detection for clinical-grade histopathological diagnostics

    Jonas Dippel, Niklas Prenißl, Julius Hense, Philipp Liznerski, Tobias Winterhoff, Simon Schallenberg, Marius Kloft, Oliver Buchstab, David Horst, Maximilian Alber, Lukas Ruff, Klaus-Robert Müller, and Frederick Klauschen. Ai-based anomaly detection for clinical-grade histopath...

    2024

  4. [12]

    An image is worth 16x16 words: Transformers for image recognition at scale

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at...

    2021

  5. [13]

    Phikon-v2, A large and public feature extractor for biomarker prediction, September 2024

    Alexandre Filiot, Paul Jacob, Alice Mac Kain, and Charlie Saillard. Phikon-v2, A large and public feature extractor for biomarker prediction, September 2024. arXiv:2409.09173

    2024 arXiv

  6. [14]

    eva: Evaluation framework for pathology foundation models

    Ioannis Gatopoulos, Nicolas Känzig, Roman Moser, and Sebastian Otálora. eva: Evaluation framework for pathology foundation models. In Medical Imaging with Deep Learning, 2024

    2024

  7. [15]

    Attention-based deep multiple instance learning

    Maximilian Ilse, Jakub Tomczak, and Max Welling. Attention-based deep multiple instance learning. In International Conference on Machine Learning, pages 2127–2136, 2018

    2018

  8. [16]

    Song, Ming Y

    Guillaume Jaume, Paul Doucet, Andrew H. Song, Ming Y . Lu, Cristina Almagro Pérez, Sophia J Wagner, Anurag Jayant Vaidya, Richard J. Chen, Drew FK Williamson, Ahrong Kim, and Faisal Mahmood. HEST-1k: A dataset for spatial transcriptomics and histology image analysis. In The Th...

    2024

  9. [17]

    Hipp, Darren Fahy, Benjamin Glass, Eric Walk, John Abel, Harsha Vardhan pokkalla, Andrew H

    Dinkar Juyal, Harshith Padigela, Chintan Shah, Daniel Shenker, Natalia Harguindeguy, Yi Liu, Blake Martin, Yibo Zhang, Michael Nercessian, Miles Markey, Isaac Finberg, Kelsey Luu, Daniel Borders, Syed Ashar Javed, Emma L Krause, Raymond Biju, Aashish Sood, Allen Ma, Jackson Ny...

    2024

  10. [18]

    Champkit: A framework for rapid evaluation of deep neural networks for patch-based histopathology classification

    Jakub R Kaczmarzyk, Rajarsi Gupta, Tahsin M Kurc, Shahira Abousamra, Joel H Saltz, and Peter K Koo. Champkit: A framework for rapid evaluation of deep neural networks for patch-based histopathology classification. Computer methods and programs in biomedicine, 239:107631, 2023

    2023

  11. [19]

    Benchmarking self-supervised learning on diverse pathology datasets

    Mingu Kang, Heon Song, Seonwook Park, Donggeun Yoo, and Sérgio Pereira. Benchmarking self-supervised learning on diverse pathology datasets. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2023

    2023

  12. [20]

    100,000 histological images of human colorectal cancer and healthy tissue (v0.1) [Data set]

    Jakob Nikolas Kather, Niels Halama, and Alexander Marx. 100,000 histological images of human colorectal cancer and healthy tissue (v0.1) [Data set]. Zenodo. https://doi.org/10. 5281/zenodo.1214456, May 2018

    2018

  13. [21]

    Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

    Jakob Nikolas Kather, Johannes Krisam, Pornpimol Charoentong, Tom Luedde, Esther Herpel, Cleo-Aron Weis, Timo Gaiser, Alexander Marx, Nektarios A Valous, Dyke Ferber, et al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multic...

    2019

  14. [22]

    Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

    Jakob Nikolas Kather, Alexander T Pearson, Niels Halama, Dirk Jäger, Jeremias Krause, Sven H Loosen, Alexander Marx, Peter Boor, Frank Tacke, Ulf Peter Neumann, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Natu...

    2019

  15. [23]

    Patient-level proteomic network prediction by ex- plainable artificial intelligence

    Philipp Keyl, Michael Bockmayr, Daniel Heim, Gabriel Dernbach, Grégoire Montavon, Klaus- Robert Müller, and Frederick Klauschen. Patient-level proteomic network prediction by ex- plainable artificial intelligence. NPJ Precision Oncology, 6(1):35, 2022

    2022

  16. [24]

    Toward explainable artificial intelligence for precision pathology

    Frederick Klauschen, Jonas Dippel, Philipp Keyl, Philipp Jurmeister, Michael Bockmayr, Andreas Mock, Oliver Buchstab, Maximilian Alber, Lukas Ruff, Grégoire Montavon, et al. Toward explainable artificial intelligence for precision pathology. Annual Review of Pathology: Mechani...

    2024

  17. [25]

    Universal encoding of pan-cancer histology by deep texture representations

    Daisuke Komura, Akihiro Kawabe, Keisuke Fukuta, Kyohei Sano, Toshikazu Umezaki, Hi- rotomo Koda, Ryohei Suzuki, Ken Tominaga, Mieko Ochi, Hiroki Konishi, et al. Universal encoding of pan-cancer histology by deep texture representations. Cell Reports, 38(9), 2022

    2022

  18. [26]

    Unsupervised foundation model-agnostic slide-level representation learning, 2024

    Tim Lenz, Peter Neidlinger, Marta Ligero, Georg Wölflein, Marko van Treeck, and Jakob Niko- las Kather. Unsupervised foundation model-agnostic slide-level representation learning, 2024. arXiv:2411.13623

    2024 arXiv

  19. [27]

    Decoupled weight decay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2019

    2019

  20. [28]

    A visual-language foundation model for computational pathology

    Ming Y Lu, Bowen Chen, Drew FK Williamson, Richard J Chen, Ivy Liang, Tong Ding, Guil- laume Jaume, Igor Odintsov, Long Phi Le, Georg Gerber, et al. A visual-language foundation model for computational pathology. Nature Medicine, 30:863–874, 2024

    2024

  21. [29]

    Hibou: A Family of Foundational Vision Transformers for Pathology, June 2024

    Dmitry Nechaev, Alexey Pchelnikov, and Ekaterina Ivanova. Hibou: A Family of Foundational Vision Transformers for Pathology, June 2024. arXiv:2406.05074

    2024 arXiv

  22. [30]

    Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V . V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel HAZIZA, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rab...

    2024

  23. [31]

    Pedregosa, G

    F. Pedregosa, G. Varoquaux, A. Gramfort, V . Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V . Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learni...

    2011

  24. [32]

    From pixels to gigapixels: Bridging local inductive bias and long-range dependencies with pixel-mamba, 2024

    Zhongwei Qiu, Hanqing Chao, Tiancheng Lin, Wanxing Chang, Zijiang Yang, Wenpei Jiao, Yixuan Shen, Yunshuo Zhang, Yelin Yang, Wenbin Liu, Hui Jiang, Yun Bian, Ke Yan, Dakai Jin, and Le Lu. From pixels to gigapixels: Bridging local inductive bias and long-range dependencies with...

    2024 arXiv

  25. [33]

    Clinical validation of artificial intelligence–augmented pathology diagnosis demonstrates significant gains in diagnostic accuracy in prostate cancer detection

    Patricia Raciti, Jillian Sue, Juan A Retamero, Rodrigo Ceballos, Ran Godrich, Jeremy D Kunz, Adam Casson, Dilip Thiagarajan, Zahra Ebrahimzadeh, Julian Viret, et al. Clinical validation of artificial intelligence–augmented pathology diagnosis demonstrates significant gains in ...

    2023

  26. [34]

    H-optimus-0

    Charlie Saillard, Rodolphe Jenatton, Felipe Llinares-López, Zelda Mariet, David Cahané, Eric Durand, and Jean-Philippe Vert. H-optimus-0. https://github.com/bioptimus/ releases/tree/main/models/h-optimus/v0, 2024

    2024

  27. [35]

    Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images

    Joel Saltz, Rajarsi Gupta, Le Hou, Tahsin Kurc, Pankaj Singh, Vu Nguyen, Dimitris Samaras, Kenneth R Shroyer, Tianhao Zhao, Rebecca Batiste, et al. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell re...

    2018

  28. [36]

    Kunz, Juan A

    George Shaikovski, Adam Casson, Kristen Severson, Eric Zimmermann, Yi Kan Wang, Jeremy D. Kunz, Juan A. Retamero, Gerard Oakley, David Klimstra, Christopher Kanan, Matthew Hanna, Michal Zelechowski, Julian Viret, Neil Tenenholtz, James Hall, Nicolo Fusi, 8 Razik Yousfi, Peter ...

    2024 arXiv

  29. [37]

    Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling

    Bastiaan S. Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling. Rotation equivariant cnns for digital pathology. In Alejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López, and Gabor Fichtinger, editors, Medical Image Computing and Com...

    2018

  30. [38]

    Transformer-based unsupervised contrastive learning for histopathological image classification

    Xiyue Wang, Sen Yang, Jun Zhang, Minghui Wang, Jing Zhang, Wei Yang, Junzhou Huang, and Xiao Han. Transformer-based unsupervised contrastive learning for histopathological image classification. Medical Image Analysis, 81:102559, October 2022

    2022

  31. [39]

    Jackson, Jun Zhang, Deborah Dillon, Nancy U

    Xiyue Wang, Junhan Zhao, Eliana Marostica, Wei Yuan, Jietian Jin, Jiayu Zhang, Ruijiang Li, Hongping Tang, Kanran Wang, Yu Li, Fang Wang, Yulong Peng, Junyou Zhu, Jing Zhang, Christopher R. Jackson, Jun Zhang, Deborah Dillon, Nancy U. Lin, Lynette Sholl, Thomas Denize, David M...

    2024

  32. [40]

    A petri dish for histopathology image analysis

    Jerry Wei, Arief Suriawinata, Bing Ren, Xiaoying Liu, Mikhail Lisovsky, Louis Vaickus, Charles Brown, Michael Baker, Naofumi Tomita, Lorenzo Torresani, et al. A petri dish for histopathology image analysis. In Artificial Intelligence in Medicine: 19th International Conference ...

    2021

  33. [41]

    Wright, Ari Robicsek, Brian Piening, Carlo Bifulco, Sheng Wang, and Hoifung Poon

    Hanwen Xu, Naoto Usuyama, Jaspreet Bagga, Sheng Zhang, Rajesh Rao, Tristan Naumann, Cliff Wong, Zelalem Gero, Javier González, Yu Gu, Yanbo Xu, Mu Wei, Wenhui Wang, Shuming Ma, Furu Wei, Jianwei Yang, Chunyuan Li, Jianfeng Gao, Jaylen Rosemon, Tucker Bower, Soohee Lee, Roshant...

    2024

  34. [42]

    Virchow 2: Scaling Self-Supervised Mixed Magnification Models in Pathology, August 2024

    Eric Zimmermann, Eugene V orontsov, Julian Viret, Adam Casson, Michal Zelechowski, George Shaikovski, Neil Tenenholtz, James Hall, Thomas Fuchs, Nicolo Fusi, Siqi Liu, and Kristen Severson. Virchow 2: Scaling Self-Supervised Mixed Magnification Models in Pathology, August 2024...

    2024 arXiv

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.