mlsanityData to 14 Oct 2026

Unleashing the Infinity Power of Geometry: A Novel Geometry-Aware Transformer (GOAT) for Whole Slide Histopathology Image Analysis

arXiv 2402.05373 ↗First posted 8 Feb 2024eess.IV cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

The histopathology analysis is of great significance for the diagnosis and prognosis of cancers, however, it has great challenges due to the enormous heterogeneity of gigapixel whole slide images (WSIs) and the intricate representation of pathological features. However, recent methods have not adequately exploited geometrical representation in WSIs which is significant in disease diagnosis. Therefore, we proposed a novel weakly-supervised framework, Geometry-Aware Transformer (GOAT), in which we urge the model to pay attention to the geometric characteristics within the tumor microenvironment which often serve as potent indicators. In addition, a context-aware attention mechanism is designed to extract and enhance the morphological features within WSIs.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 12 of its 18 references that are in the map. First posted on arXiv 8 Feb 2024.

Builds on

12 of 18 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  3. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  4. 2020A Generalization of Transformer Networks to Graphscited by 661
  5. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 478
  6. 2017Topology Adaptive Graph Convolutional Networkscited by 113
  7. 2019Streaming convolutional neural networks for end-to-end learning with multi-megapixel imagescited by 41
  8. 2019Weakly Supervised Prostate TMA Classification via Graph Convolutional Networkscited by 17
  9. 2022Gigapixel Whole-Slide Images Classification using Locally Supervised Learningcited by 15
  10. 2019Patch Transformer for Multi-tagging Whole Slide Histopathology Imagescited by 13
  11. 2023Graph Propagation Transformer for Graph Representation Learningcited by 9
  12. 2023MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Featurescited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025MurreNet: Modeling Holistic Multimodal Interactions Between Histopathology and Genomic Profiles for Survival Predictioncited by 3
  2. 2025A Hierarchical Geometry-guided Transformer for Histological Subtyping of Primary Liver Cancercited by 0
  3. 2026Synergistic Information Disentanglement for Omni-modal Slide Representation Learning in Computational Pathologycited by 0