Explainable and Position-Aware Learning in Digital Pathology

• URL: http://arxiv.org/abs/2306.08198v1
• Date: Wed, 14 Jun 2023 01:53:17 GMT
• Title: Explainable and Position-Aware Learning in Digital Pathology
• Authors: Milan Aryal and Nasim Yahyasoltani
• Abstract summary: In this work, classification of cancer from WSIs is performed with positional embedding and graph attention. A comparison of the proposed method with leading approaches in cancer diagnosis and grading verify improved performance. The identification of cancerous regions in WSIs is another critical task in cancer diagnosis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Encoding whole slide images (WSI) as graphs is well motivated since it makes it possible for the gigapixel resolution WSI to be represented in its entirety for the purpose of graph learning. To this end, WSIs can be broken into smaller patches that represent the nodes of the graph. Then, graph-based learning methods can be utilized for the grading and classification of cancer. Message passing among neighboring nodes is the foundation of graph-based learning methods. However, they do not take into consideration any positional information for any of the patches, and if two patches are found in topologically isomorphic neighborhoods, their embeddings are nearly similar to one another. In this work, classification of cancer from WSIs is performed with positional embedding and graph attention. In order to represent the positional embedding of the nodes in graph classification, the proposed method makes use of spline convolutional neural networks (CNN). The algorithm is then tested with the WSI dataset for grading prostate cancer and kidney cancer. A comparison of the proposed method with leading approaches in cancer diagnosis and grading verify improved performance. The identification of cancerous regions in WSIs is another critical task in cancer diagnosis. In this work, the explainability of the proposed model is also addressed. A gradient-based explainbility approach is used to generate the saliency mapping for the WSIs. This can be used to look into regions of WSI that are responsible for cancer diagnosis thus rendering the proposed model explainable.

Related papers

• 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)
• C2P-GCN: Cell-to-Patch Graph Convolutional Network for Colorectal Cancer Grading [2.570529808612886]
  Graph-based learning approaches are increasingly favored for grading colorectal cancer histology images. Recent graph-based techniques involve dividing whole slide images into smaller or medium-sized patches, and then building graphs on each patch for direct use in training. This method fails to capture the tissue structure information present in an entire WSI and relies on training from a significantly large dataset of image patches. We propose a novel cell-to-patch graph convolutional network (C2P-GCN), which is a two-stage graph formation-based approach.
  arXiv  Detail & Related papers  (2024-03-08T00:15:43Z)
• 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)
• Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning [48.02011627390706]
  We train an attention-based MIL and calculate a confidence metric for every image in the dataset to select the most uncertain WSIs for expert annotation. With a novel attention guiding loss, this leads to an accuracy boost of the trained models with few regions annotated for each class. It may in the future serve as an important contribution to train MIL models in the clinically relevant context of cancer classification in histopathology.
  arXiv  Detail & Related papers  (2023-03-02T15:18:58Z)
• 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)
• A graph-transformer for whole slide image classification [11.968797693846476]
  We present a Graph-Transformer (GT) that fuses a graph-based representation of an whole slide image (WSI) and a vision transformer for processing pathology images, called GTP, to predict disease grade. Our findings demonstrate GTP as an interpretable and effective deep learning framework for WSI-level classification.
  arXiv  Detail & Related papers  (2022-05-19T16:32:10Z)
• Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer [7.607669435880715]
  Graph based features are applied to gastric cancer histopathology microscopic image analysis. It is found that using U-Net to segment tissue areas, then extracting graph based features, and finally using RBF SVM classifier gives the optimal results with 94.29%.
  arXiv  Detail & Related papers  (2022-05-17T16:16:29Z)
• Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning [88.60285937702304]
  This paper proposes a novel spatial-spectral HSI classification method via multiple random anchor graphs ensemble learning (RAGE) Firstly, the local binary pattern is adopted to extract the more descriptive features on each selected band, which preserves local structures and subtle changes of a region. Secondly, the adaptive neighbors assignment is introduced in the construction of anchor graph, to reduce the computational complexity.
  arXiv  Detail & Related papers  (2021-03-25T09:31:41Z)
• Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification [63.56018768401328]
  We propose a Multi-level Graph Convolutional Network (GCN) with Automatic Graph Learning method (MGCN-AGL) for HSI classification. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions. Our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level.
  arXiv  Detail & Related papers  (2020-09-19T09:26:20Z)
• Sequential Graph Convolutional Network for Active Learning [53.99104862192055]
  We propose a novel pool-based Active Learning framework constructed on a sequential Graph Convolution Network (GCN) With a small number of randomly sampled images as seed labelled examples, we learn the parameters of the graph to distinguish labelled vs unlabelled nodes. We exploit these characteristics of GCN to select the unlabelled examples which are sufficiently different from labelled ones.
  arXiv  Detail & Related papers  (2020-06-18T00:55:10Z)
• Unifying Graph Convolutional Neural Networks and Label Propagation [73.82013612939507]
  We study the relationship between LPA and GCN in terms of two aspects: feature/label smoothing and feature/label influence. Based on our theoretical analysis, we propose an end-to-end model that unifies GCN and LPA for node classification. Our model can also be seen as learning attention weights based on node labels, which is more task-oriented than existing feature-based attention models.
  arXiv  Detail & Related papers  (2020-02-17T03:23:13Z)

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.