Slide-based Graph Collaborative Training for Histopathology Whole Slide Image Analysis

• URL: http://arxiv.org/abs/2410.10260v1
• Date: Mon, 14 Oct 2024 08:14:40 GMT
• Title: Slide-based Graph Collaborative Training for Histopathology Whole Slide Image Analysis
• Authors: Jun Shi, Tong Shu, Zhiguo Jiang, Wei Wang, Haibo Wu, Yushan Zheng,
• Abstract summary: We propose a generic WSI analysis pipeline SlideGCD that can be adapted to any existing Multiple Instance Learning (MIL) frameworks. With the new paradigm, the prior knowledge of cancer development can participate in the end-to-end workflow.
• Score: 7.823674912857107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The development of computational pathology lies in the consensus that pathological characteristics of tumors are significant guidance for cancer diagnostics. Most existing research focuses on the inner-contextual information within each WSI yet ignores the possible inter-correlations between slides. As the development of tumors is a continuous process involving a series of histological, morphological, and genetic changes that accumulate over time, the similarities and differences between WSIs across various stages, grades, locations and patients should potentially contribute to the representation of WSIs and deserve to be taken into account in WSI modeling. To verify the advancement of introducing the slide inter-correlations into the representation learning of WSIs, we proposed a generic WSI analysis pipeline SlideGCD that can be adapted to any existing Multiple Instance Learning (MIL) frameworks and improve their performance. With the new paradigm, the prior knowledge of cancer development can participate in the end-to-end workflow, which concurrently initializes and refines the slide representation, as a guide for message passing in the slide-based graph. Extensive comparisons and experiments are conducted to validate the effectiveness and robustness of the proposed pipeline across 4 different tasks, including cancer subtyping, cancer staging, survival prediction, and gene mutation prediction, with 7 representative SOTA WSI analysis frameworks as backbones.

Related papers

• Continually Evolved Multimodal Foundation Models for Cancer Prognosis [50.43145292874533]
  Cancer prognosis is a critical task that involves predicting patient outcomes and survival rates. Previous studies have integrated diverse data modalities, such as clinical notes, medical images, and genomic data, leveraging their complementary information. Existing approaches face two major limitations. First, they struggle to incorporate newly arrived data with varying distributions into training, such as patient records from different hospitals. Second, most multimodal integration methods rely on simplistic concatenation or task-specific pipelines, which fail to capture the complex interdependencies across modalities.
  arXiv  Detail & Related papers  (2025-01-30T06:49:57Z)
• Enhanced MRI Representation via Cross-series Masking [48.09478307927716]
  Cross-Series Masking (CSM) Strategy for effectively learning MRI representation in a self-supervised manner. Method achieves state-of-the-art performance on both public and in-house datasets.
  arXiv  Detail & Related papers  (2024-12-10T10:32:09Z)
• Multimodal Outer Arithmetic Block Dual Fusion of Whole Slide Images and Omics Data for Precision Oncology [6.418265127069878]
  We propose the use of omic embeddings during early and late fusion to capture complementary information from local (patch-level) to global (slide-level) interactions. This dual fusion strategy enhances interpretability and classification performance, highlighting its potential for clinical diagnostics.
  arXiv  Detail & Related papers  (2024-11-26T13:25:53Z)
• SlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image Classification [4.994171453938461]
  Recent research mainly focuses on the inner-contextual information in a single WSI, ignoring the correlations between slides. We propose a generic WSI analysis pipeline SlideGCD that considers the existing multi-instance learning (MIL) methods as the backbone and forge the WSI classification task as a node classification problem. The consistent performance boosting, brought by SlideGCD, of four previous state-of-the-art MIL methods is observed on two TCGA benchmark datasets.
  arXiv  Detail & Related papers  (2024-07-12T03:32:13Z)
• A self-supervised framework for learning whole slide representations [52.774822784847565]
  We present Slide Pre-trained Transformers (SPT) for gigapixel-scale self-supervision of whole slide images. We benchmark SPT visual representations on five diagnostic tasks across three biomedical microscopy datasets.
  arXiv  Detail & Related papers  (2024-02-09T05:05:28Z)
• GRASP: GRAph-Structured Pyramidal Whole Slide Image Representation [4.5869791542071]
  We present GRASP, a graph-structured multi-magnification framework for processing whole slide images (WSIs) in digital pathology. Our approach is designed to emulate the pathologist's behavior in handling WSIs and benefits from the hierarchical structure of WSIs. GRASP, which introduces a convergence-based node aggregation instead of traditional pooling mechanisms, outperforms state-of-the-art methods over two distinct cancer datasets.
  arXiv  Detail & Related papers  (2024-02-06T00:03:44Z)
• Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning [78.49090351193269]
  We propose a novel graph-based framework to leverage the inter-relationships among different types of nuclei for WSI analysis. Specifically, we formulate the WSI as a heterogeneous graph with "nucleus-type" attribute to each node and a semantic attribute similarity to each edge. Our framework outperforms the state-of-the-art methods with considerable margins on various tasks.
  arXiv  Detail & Related papers  (2023-07-09T14:43:40Z)
• Context-Aware Self-Supervised Learning of Whole Slide Images [0.0]
  A novel two-stage learning technique is presented in this work. A graph representation capturing all dependencies among regions in the WSI is very intuitive. The entire slide is presented as a graph, where the nodes correspond to the patches from the WSI. The proposed framework is then tested using WSIs from prostate and kidney cancers.
  arXiv  Detail & Related papers  (2023-06-07T20:23:05Z)
• Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics [63.76637479503006]
  Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical. This paper proposes a hierarchical-based multimodal transformer framework that learns a hierarchical mapping between pathology images and corresponding genes. Our architecture requires fewer GPU resources compared with benchmark methods while maintaining better WSI representation ability.
  arXiv  Detail & Related papers  (2022-11-29T23:47:56Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.