HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification

Pati, Pushpak; Jaume, Guillaume; Fernandes, Lauren Alisha; Foncubierta–Rodríguez, Antonio; Feroce, Florinda; Anniciello, Anna Maria; Scognamiglio, Giosuè; Riccio, Daniel; Bonito, Maurizio Di; Pietro, Giuseppe De; Botti, Gerardo; Göksel, Orçun; Thiran, Jean‐Philippe; Frucci, Maria; Gabrani, Maria

doi:10.1007/978-3-030-60365-6_20

Cited by 49 publications

(51 citation statements)

References 18 publications

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“…We employ IFEXPLAINER to explain the predictions of the GNN on breast cancer subtyping. Qualitative and quantitative results on BRACS dataset [25] show the superior performance of the proposed method.…”

Section: Introductionmentioning

confidence: 89%

“…To exploit the hierarchical graph structure, Hact-net builds the tissue graph and cell graph to capture the cellular attributes at different levels [25]. Slide-Graph enriches the topological context of nodes via aggregating the local features in the cell graph [21].…”

Section: Gnns In Digital Pathologymentioning

confidence: 99%

“…We employ the BReAst Cancer Subtyping (BRACS) dataset [25] for the experiments. It consists of 4931 RoIs at 40× magnification sampled from 325 H&E stained breast carcinoma whole-slides, and each RoI contains 1468 nuclei on average.…”

Section: Dataset and Settingsmentioning

confidence: 99%

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Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows

Yu¹,

Xu²,

He³

2021

Preprint

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As Graph Neural Networks (GNNs) are widely adopted in digital pathology, there is increasing attention to developing explanation models (explainers) of GNNs for improved transparency in clinical decisions. Existing explainers discover an explanatory subgraph relevant to the prediction. However, such a subgraph is insufficient to reveal all the critical biological substructures for the prediction because the prediction will remain unchanged after removing that subgraph. Hence, an explanatory subgraph should be not only necessary for prediction, but also sufficient to uncover the most predictive regions for the explanation. Such explanation requires a measurement of information transferred from different input subgraphs to the predictive output, which we define as information flow. In this work, we address these key challenges and propose IFEXPLAINER, which generates a necessary and sufficient explanation for GNNs. To evaluate the information flow within GNN's prediction, we first propose a novel notion of predictiveness, named f -information, which is directional and incorporates the realistic capacity of the GNN model. Based on it, IFEXPLAINER generates the explanatory subgraph with maximal information flow to the prediction. Meanwhile, it minimizes the information flow from the input to the predictive result after removing the explanation. Thus, the produced explanation is necessarily important to the prediction and sufficient to reveal the most crucial substructures. We evaluate IFEXPLAINER to interpret GNN's predictions on breast cancer subtyping. Experimental results on the BRACS dataset show the superior performance of the proposed method.

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Section: Introductionmentioning

confidence: 89%

Section: Gnns In Digital Pathologymentioning

confidence: 99%

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Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows

Yu¹,

Xu²,

He³

2021

Preprint

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“…Breast cancer 11 [16], [25]- [34] Colorectal cancer 6 [15], [35]- [39] Prostate cancer 3 [14], [27], [ Total 28…”

Section: Application #Applications Referencementioning

confidence: 99%

“…C. Hierarchical cell-to-tissue graph representation for breast cancer. Images adapted from [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Graph-Based Deep Learning for Computational Histopathology

Ahmedt-Aristizabal¹,

Armin²,

Denman³

et al. 2021

Preprint

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With the remarkable success of representation learning for prediction problems, we have witnessed a rapid expansion of the use of machine learning and deep learning for the analysis of digital pathology and biopsy image patches. However, traditional learning over patch-wise features using convolutional neural networks limits the model when attempting to capture global contextual information. The phenotypical and topological distribution of constituent histological entities play a critical role in tissue diagnosis. As such, graph data representations and deep learning have attracted significant attention for encoding tissue representations, and capturing intra-and inter-entity level interactions. In this review, we provide a conceptual grounding of graph-based deep learning and discuss its current success for tumor localization and classification, tumor invasion and staging, image retrieval, and survival prediction. We provide an overview of these methods in a systematic manner organized by the graph representation of the input image including whole slide images and tissue microarrays. We also outline the limitations of existing techniques, and suggest potential future advances in this domain.

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A survey on artificial intelligence in histopathology image analysis

Abdelsamea

Zidan

Senousy

et al. 2022

WIREs Data Min & Knowl

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The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning-based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field.

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HACT-Net: A Hierarchical Cell-to-Tissue Graph Neural Network for Histopathological Image Classification

Cited by 49 publications

References 18 publications

Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows

Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows

A Survey on Graph-Based Deep Learning for Computational Histopathology

A survey on artificial intelligence in histopathology image analysis

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