mlsanityData to 7 Oct 2026

ProsMAE: Multi-Source MAE Pretraining for ISUP Grade Classification

arXiv 2607.08162 ↗First posted 9 Jul 2026cs.CV cs.AI cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Whole slide images (WSIs) provide rich diagnostic information for computational pathology, but their gigapixel scale, stain variation, scanner differences, tissue artifacts, and limited expert annotation make robust model training challenging. This paper presents a multi-source Masked Autoencoder (MAE) framework, named ProsMAE, for histopathology representation learning. Tiles from Prostate cANcer graDe Assessment (PANDA), CAncer MEtastases in LYmph nOdes challeNge 2017 (CAMELYON17), and BReAst Carcinoma Subtyping (BRACS) are used for ProsMAE pretraining to expose the encoder to diverse tissue morphology and acquisition conditions. The learned encoder is transferred for International Society of Urological Pathology (ISUP) grade classification through ProsCLS, using a frozen encoder and a linear classification head. ProsMAE achieved a higher mean validation quadratic weighted kappa (QWK) than the vanilla MAE frozen linear-probe baseline under the evaluated disjoint PANDA split. Repeated-split evaluation remains necessary to further establish robustness across split compositions.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 18 references that are in the map. First posted on arXiv 9 Jul 2026.

Builds on

11 of 18 references.

References · 11

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  3. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  4. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 738
  5. 2019Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathologycited by 172
  6. 2020Self supervised contrastive learning for digital histopathologycited by 146
  7. 2021BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Imagescited by 130
  8. 2022Mitosis domain generalization in histopathology images -- The MIDOG challengecited by 66
  9. 2024A Clinical Benchmark of Public Self-Supervised Pathology Foundation Modelscited by 64
  10. 2026MAESIL: Masked Autoencoder for Enhanced Self-supervised Medical Image Learningcited by 3
  11. 2026Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Predictioncited by 1

Built on by

Not cited yet.