HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification
| Autor: | Lauren Alisha Fernandes, Maria Gabrani, Maria Frucci, Gerardo Botti, Giuseppe De Pietro, Florinda Feroce, Maurizio Di Bonito, Daniel Riccio, Jean-Philippe Thiran, Pushpak Pati, Orcun Goksel, Antonio Foncubierta-Rodríguez, Anna Maria Anniciello, Guillaume Jaume, Nadia Brancati, Giosuè Scognamiglio |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Contextual image classification business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) Digital Pathology Computer Science - Computer Vision and Pattern Recognition Representation (systemics) Pattern recognition medicine.disease Cell morphology Convolutional neural network Breast cancer Annotated Tissue Encoding (memory) Cancer Grading Prior probability medicine Graph Neural Networks Artificial intelligence business |
| Zdroj: | Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis ISBN: 9783030603649 UNSURE/GRAIL@MICCAI GRAIL 2020, in conjunction with MICCAI 2020, pp. 208–219, Lima, Perù, 08/10/2020 info:cnr-pdr/source/autori:P. Pati, G. Jaume, L. Alisha Fernandes, A. Foncubierta, F. Feroce, A. M. Anniciello, G. Scognamiglio, N. Brancati, D.Riccio, M. Di Bonito, G. De Pietro, G. Botti, O. Goksel, J. P. Thiran, M. Frucci, M. Gabrani/congresso_nome:GRAIL 2020, in conjunction with MICCAI 2020,/congresso_luogo:Lima, Perù/congresso_data:08%2F10%2F2020/anno:2020/pagina_da:208/pagina_a:219/intervallo_pagine:208–219 |
| DOI: | 10.1007/978-3-030-60365-6_20 |
| Popis: | Cancer diagnosis, prognosis, and therapeutic response prediction are heavily influenced by the relationship between the histopathological structures and the function of the tissue. Recent approaches acknowledging the structure-function relationship, have linked the structural and spatial patterns of cell organization in tissue via cell-graphs to tumor grades. Though cell organization is imperative, it is insufficient to entirely represent the histopathological structure. We propose a novel hierarchical cell-to-tissue-graph (HACT) representation to improve the structural depiction of the tissue. It consists of a low-level cell-graph, capturing cell morphology and interactions, a high-level tissue-graph, capturing morphology and spatial distribution of tissue parts, and cells-to-tissue hierarchies, encoding the relative spatial distribution of the cells with respect to the tissue distribution. Further, a hierarchical graph neural network (HACT-Net) is proposed to efficiently map the HACT representations to histopathological breast cancer subtypes. We assess the methodology on a large set of annotated tissue regions of interest from H\&E stained breast carcinoma whole-slides. Upon evaluation, the proposed method outperformed recent convolutional neural network and graph neural network approaches for breast cancer multi-class subtyping. The proposed entity-based topological analysis is more inline with the pathological diagnostic procedure of the tissue. It provides more command over the tissue modelling, therefore encourages the further inclusion of pathological priors into task-specific tissue representation. |
| Databáze: | OpenAIRE |
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