title: PathoHR: Breast Cancer Survival Prediction on High-Resolution
    Pathological Images
summary: Breast cancer survival prediction in computational pathology presents a
  remarkable challenge due to tumor heterogeneity. For instance, different
  regions of the same tumor in the pathology image can show distinct
  morphological and molecular characteristics. This makes it difficult to extract
  representative features from whole slide images (WSIs) that truly reflect the
  tumor's aggressive potential and likely survival outcomes. In this paper, we
  present PathoHR, a novel pipeline for accurate breast cancer survival
  prediction that enhances any size of pathological images to enable more
  effective feature learning. Our approach entails (1) the incorporation of a
  plug-and-play high-resolution Vision Transformer (ViT) to enhance patch-wise
  WSI representation, enabling more detailed and comprehensive feature
  extraction, (2) the systematic evaluation of multiple advanced similarity
  metrics for comparing WSI-extracted features, optimizing the representation
  learning process to better capture tumor characteristics, (3) the demonstration
  that smaller image patches enhanced follow the proposed pipeline can achieve
  equivalent or superior prediction accuracy compared to raw larger patches,
  while significantly reducing computational overhead. Experimental findings
  valid that PathoHR provides the potential way of integrating enhanced image
  resolution with optimized feature learning to advance computational pathology,
  offering a promising direction for more accurate and efficient breast cancer
  survival prediction. Code will be available at
  https://github.com/AIGeeksGroup/PathoHR.
paper_authors._id: 67e52a53c17e104ac467acad
paper_authors.name: Yang Luo
paper_authors._id: 67e52a53c17e104ac467acae
paper_authors.name: Shiru Wang
paper_authors._id: 67e52a53c17e104ac467acaf
paper_authors.name: Jun Liu
paper_authors._id: 67e52a53c17e104ac467acb0
paper_authors.name: Jiaxuan Xiao
paper_authors._id: 67e52a53c17e104ac467acb1
paper_authors.name: Rundong Xue
paper_authors._id: 67e52a53c17e104ac467acb2
paper_authors.name: Zeyu Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-27T14:25:30.015Z
paper_authors._id: 64ec877bb93654d4ca5c92e9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64ec877bb93654d4ca5c92e9/GvHk_KSdE9Rhnk_o-NaZX.jpeg
paper_authors.fullname: Zeyu Zhang
paper_authors.type: user
paper_authors.user: SteveZeyuZhang
paper_authors._id: 67e52a53c17e104ac467acb3
paper_authors.name: Hao Zhang
paper_authors._id: 67e52a53c17e104ac467acb4
paper_authors.name: Yu Lu
paper_authors._id: 67e52a53c17e104ac467acb5
paper_authors.name: Yang Zhao
paper_authors._id: 67e52a53c17e104ac467acb6
paper_authors.name: Yutong Xie
paper_title: PathoHR: Breast Cancer Survival Prediction on High-Resolution
    Pathological Images
paper_summary: Breast cancer survival prediction in computational pathology presents a
  remarkable challenge due to tumor heterogeneity. For instance, different
  regions of the same tumor in the pathology image can show distinct
  morphological and molecular characteristics. This makes it difficult to extract
  representative features from whole slide images (WSIs) that truly reflect the
  tumor's aggressive potential and likely survival outcomes. In this paper, we
  present PathoHR, a novel pipeline for accurate breast cancer survival
  prediction that enhances any size of pathological images to enable more
  effective feature learning. Our approach entails (1) the incorporation of a
  plug-and-play high-resolution Vision Transformer (ViT) to enhance patch-wise
  WSI representation, enabling more detailed and comprehensive feature
  extraction, (2) the systematic evaluation of multiple advanced similarity
  metrics for comparing WSI-extracted features, optimizing the representation
  learning process to better capture tumor characteristics, (3) the demonstration
  that smaller image patches enhanced follow the proposed pipeline can achieve
  equivalent or superior prediction accuracy compared to raw larger patches,
  while significantly reducing computational overhead. Experimental findings
  valid that PathoHR provides the potential way of integrating enhanced image
  resolution with optimized feature learning to advance computational pathology,
  offering a promising direction for more accurate and efficient breast cancer
  survival prediction. Code will be available at
  https://github.com/AIGeeksGroup/PathoHR.
paper_ai_summary: PathoHR enhances breast cancer survival prediction using a high-resolution Vision Transformer and optimized similarity metrics, achieving accuracy with smaller image patches while reducing computational costs.
paper_ai_keywords: Vision Transformer ViT whole slide images WSI patch-wise similarity metrics feature learning tumor characteristics