Finding Regions of Interest in Whole Slide Images Using Multiple Instance Learning

• URL: http://arxiv.org/abs/2404.01446v2
• Date: Thu, 11 Apr 2024 06:58:18 GMT
• Title: Finding Regions of Interest in Whole Slide Images Using Multiple Instance Learning
• Authors: Martim Afonso, Praphulla M. S. Bhawsar, Monjoy Saha, Jonas S. Almeida, Arlindo L. Oliveira,
• Abstract summary: Whole Slide Images (WSI) represent a particular challenge to AI-based/AI-mediated analysis because pathology labeling is typically done at slide-level, instead of tile-level. We propose a weakly supervised Multiple Instance Learning (MIL) approach to accurately predict the overall cancer phenotype.
• Score: 0.23301643766310368
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Whole Slide Images (WSI), obtained by high-resolution digital scanning of microscope slides at multiple scales, are the cornerstone of modern Digital Pathology. However, they represent a particular challenge to AI-based/AI-mediated analysis because pathology labeling is typically done at slide-level, instead of tile-level. It is not just that medical diagnostics is recorded at the specimen level, the detection of oncogene mutation is also experimentally obtained, and recorded by initiatives like The Cancer Genome Atlas (TCGA), at the slide level. This configures a dual challenge: a) accurately predicting the overall cancer phenotype and b) finding out what cellular morphologies are associated with it at the tile level. To address these challenges, a weakly supervised Multiple Instance Learning (MIL) approach was explored for two prevalent cancer types, Invasive Breast Carcinoma (TCGA-BRCA) and Lung Squamous Cell Carcinoma (TCGA-LUSC). This approach was explored for tumor detection at low magnification levels and TP53 mutations at various levels. Our results show that a novel additive implementation of MIL matched the performance of reference implementation (AUC 0.96), and was only slightly outperformed by Attention MIL (AUC 0.97). More interestingly from the perspective of the molecular pathologist, these different AI architectures identify distinct sensitivities to morphological features (through the detection of Regions of Interest, RoI) at different amplification levels. Tellingly, TP53 mutation was most sensitive to features at the higher applications where cellular morphology is resolved.

Related papers

• Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis in Breast Cancer Sections [21.75452517154339]
  We reprocessed 1,399 whole slide images (WSIs) and labels from the Camelyon-16 and Camelyon-17 datasets. Based on the sizes of re-annotated tumor regions, we upgraded the binary cancer screening task to a four-class task.
  arXiv  Detail & Related papers  (2024-11-16T09:19:24Z)
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images [3.1118773046912382]
  We propose the Context-Aware Multiple Instance Learning (CAMIL) architecture for cancer diagnosis. CAMIL incorporates neighbor-constrained attention to consider dependencies among tiles within a Whole Slide Images (WSI) and integrates contextual constraints as prior knowledge. We evaluate CAMIL on subtyping non-small cell lung cancer (TCGA-NSCLC) and detecting lymph node metastasis, achieving test AUCs of 97.5%, 95.9%, and 88.1%, respectively.
  arXiv  Detail & Related papers  (2023-05-09T10:06:37Z)
• 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 Pathologist-Informed Workflow for Classification of Prostate Glands in Histopathology [62.997667081978825]
  Pathologists diagnose and grade prostate cancer by examining tissue from needle biopsies on glass slides. Cancer's severity and risk of metastasis are determined by the Gleason grade, a score based on the organization and morphology of prostate cancer glands. This paper proposes an automated workflow that follows pathologists' textitmodus operandi, isolating and classifying multi-scale patches of individual glands.
  arXiv  Detail & Related papers  (2022-09-27T14:08:19Z)
• Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling [5.606290756924216]
  Supervised learning tasks such as cancer survival prediction from gigapixel whole slide images (WSIs) are a critical challenge in computational pathology. We develop a novel variance pooling architecture that enables a MIL model to incorporate intratumoral heterogeneity into its predictions. An empirical study with 4,479 gigapixel WSIs from the Cancer Genome Atlas shows that adding variance pooling onto MIL frameworks improves survival prediction performance for five cancer types.
  arXiv  Detail & Related papers  (2022-06-17T16:35:35Z)
• Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning [59.30734371401316]
  Medulloblastoma is the most common malignant brain cancer among children. CNN has shown promising results for MB subtype classification. We study the impact of tile size and input strategy.
  arXiv  Detail & Related papers  (2021-09-14T09:42:37Z)
• Learned super resolution ultrasound for improved breast lesion characterization [52.77024349608834]
  Super resolution ultrasound localization microscopy enables imaging of the microvasculature at the capillary level. In this work we use a deep neural network architecture that makes effective use of signal structure to address these challenges. By leveraging our trained network, the microvasculature structure is recovered in a short time, without prior PSF knowledge, and without requiring separability of the UCAs.
  arXiv  Detail & Related papers  (2021-07-12T09:04:20Z)
• Selecting Regions of Interest in Large Multi-Scale Images for Cancer Pathology [0.0]
  High resolution scans of microscopy slides offer enough information for a cancer pathologist to come to a conclusion regarding cancer presence, subtype, and severity based on measurements of features within the slide image at multiple scales and resolutions. We explore approaches based on Reinforcement Learning and Beam Search to learn to progressively zoom into the WSI to detect Regions of Interest (ROIs) in liver pathology slides containing one of two types of liver cancer, namely Hepatocellular Carcinoma (HCC) and Cholangiocarcinoma (CC) These ROIs can then be presented directly to the pathologist to aid in measurement and diagnosis or be used
  arXiv  Detail & Related papers  (2020-07-03T15:27:41Z)
• Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
  We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size. The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
  arXiv  Detail & Related papers  (2020-02-03T22:25:01Z)
• Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images [16.02231907106384]
  We develop a new CNN-based cancer subtype classification method by effectively combining multiple-instance, domain adversarial, and multi-scale learning frameworks. The classification performance was significantly better than the standard CNN or other conventional methods, and the accuracy compared favorably with that of standard pathologists.
  arXiv  Detail & Related papers  (2020-01-06T14:09:51Z)

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.