A Hierarchical Geometry-guided Transformer for Histological Subtyping of Primary Liver Cancer

• URL: http://arxiv.org/abs/2510.05657v1
• Date: Tue, 07 Oct 2025 08:10:18 GMT
• Title: A Hierarchical Geometry-guided Transformer for Histological Subtyping of Primary Liver Cancer
• Authors: Anwen Lu, Mingxin Liu, Yiping Jiao, Hongyi Gong, Geyang Xu, Jun Chen, Jun Xu,
• Abstract summary: Liver cancer is the most heterogeneous and prognostically diverse cancers of the digestive system.<n>The representation of features in Whole Slide Images (WSIs) encompasses crucial information for liver cancer histological subtyping.<n>ARGUS is proposed to advance histological subtyping in liver cancer by capturing the macro-meso-micro hierarchical information within the TME.
• Score: 9.468531938157998
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Primary liver malignancies are widely recognized as the most heterogeneous and prognostically diverse cancers of the digestive system. Among these, hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) emerge as the two principal histological subtypes, demonstrating significantly greater complexity in tissue morphology and cellular architecture than other common tumors. The intricate representation of features in Whole Slide Images (WSIs) encompasses abundant crucial information for liver cancer histological subtyping, regarding hierarchical pyramid structure, tumor microenvironment (TME), and geometric representation. However, recent approaches have not adequately exploited these indispensable effective descriptors, resulting in a limited understanding of histological representation and suboptimal subtyping performance. To mitigate these limitations, ARGUS is proposed to advance histological subtyping in liver cancer by capturing the macro-meso-micro hierarchical information within the TME. Specifically, we first construct a micro-geometry feature to represent fine-grained cell-level pattern via a geometric structure across nuclei, thereby providing a more refined and precise perspective for delineating pathological images. Then, a Hierarchical Field-of-Views (FoVs) Alignment module is designed to model macro- and meso-level hierarchical interactions inherent in WSIs. Finally, the augmented micro-geometry and FoVs features are fused into a joint representation via present Geometry Prior Guided Fusion strategy for modeling holistic phenotype interactions. Extensive experiments on public and private cohorts demonstrate that our ARGUS achieves state-of-the-art (SOTA) performance in histological subtyping of liver cancer, which provide an effective diagnostic tool for primary liver malignancies in clinical practice.

Related papers

• OmniCT: Towards a Unified Slice-Volume LVLM for Comprehensive CT Analysis [53.01523944168442]
  Clinical interpretation relies on both slice-driven local features and volume-driven spatial representations.<n>Existing Large Vision-Language Models (LVLMs) remain fragmented in CT slice versus volumetric understanding.<n>We present OmniCT, a powerful unified slice-volume LVLM for CT scenarios.
  arXiv  Detail & Related papers  (2026-02-18T00:42:41Z)
• R-GenIMA: Integrating Neuroimaging and Genetics with Interpretable Multimodal AI for Alzheimer's Disease Progression [63.97617759805451]
  Early detection of Alzheimer's disease requires models capable of integrating macro-scale neuroanatomical alterations with micro-scale genetic susceptibility.<n>We introduce R-GenIMA, an interpretable multimodal large language model that couples a novel ROI-wise vision transformer with genetic prompting.<n>R-GenIMA achieves state-of-the-art performance in four-way classification across normal cognition, subjective memory concerns, mild cognitive impairment, and AD.
  arXiv  Detail & Related papers  (2025-12-22T02:54:10Z)
• Multiscale Cross-Modal Mapping of Molecular, Pathologic, and Radiologic Phenotypes in Lipid-Deficient Clear Cell Renal CellCarcinoma [20.5770391049623]
  The lipid-deficient de-clear cell differentiated (DCCD) ccRCC subtype is associated with adverse outcomes even in early-stage disease.<n>Here, we establish a hierarchical cross-scale framework for the preoperative identification of DCCD-ccRCC.<n>PathoDCCD captured multi-scale microscopic features, from cellular morphology and tissue architecture to meso-regional organization.<n>RadioDCCD integrated complementary macroscopic information by combining whole-tumor and its habitat-subregions radiomics.
  arXiv  Detail & Related papers  (2025-12-13T23:49:41Z)
• Multimodal Sheaf-based Network for Glioblastoma Molecular Subtype Prediction [5.563171090433323]
  We propose a novel framework for structure-aware and consistent fusion of MRI and histopathology data.<n>Our model outperforms baseline methods and demonstrates robustness in incomplete or missing data scenarios.
  arXiv  Detail & Related papers  (2025-08-13T11:11:33Z)
• PAST: A multimodal single-cell foundation model for histopathology and spatial transcriptomics in cancer [26.795192024462963]
  PAST is a pan-cancer single-cell foundation model trained on 20 million paired histopathology images and single-cell transcriptomes.<n>It predicts single-cell gene expression, virtual molecular staining, and multimodal survival analysis directly from routine pathology slides.<n>Our work establishes a new paradigm for pathology foundation models, providing a versatile tool for high-resolution spatial omics, mechanistic discovery, and precision cancer research.
  arXiv  Detail & Related papers  (2025-07-08T21:51:25Z)
• MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention [57.044719143401664]
  Histopathology and transcriptomics are fundamental modalities in oncology, encapsulating the morphological and molecular aspects of the disease.<n>We present MIRROR, a novel multi-modal representation learning method designed to foster both modality alignment and retention.<n>Extensive evaluations on TCGA cohorts for cancer subtyping and survival analysis highlight MIRROR's superior performance.
  arXiv  Detail & Related papers  (2025-03-01T07:02:30Z)
• Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis [0.15206737182982252]
  PAGET (Pathological image segmentation via AGgrEgated Teachers) is a new knowledge distillation approach that integrates multiple segmentation models.<n>We demonstrate PAGET's ability to perform rapid, comprehensive TME segmentation across various tissue types and medical institutions.
  arXiv  Detail & Related papers  (2025-01-06T10:33:14Z)
• BraTS-Path Challenge: Assessing Heterogeneous Histopathologic Brain Tumor Sub-regions [29.940537460789688]
  Glioblastoma is the most common primary adult brain tumor, with a grim prognosis. The BraTS-Path challenge is to provide a systematically prepared comprehensive dataset and a benchmarking environment to develop and fairly compare deep-learning models.
  arXiv  Detail & Related papers  (2024-05-17T16:02:21Z)
• Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis [7.996257103473235]
  We propose Pathology-Genome Heterogeneous Graph (PGHG) that integrates whole slide images (WSI) and bulk RNA-Seq expression data with heterogeneous graph neural network for cancer survival analysis. The PGHG consists of biological knowledge-guided representation learning network and pathology-genome heterogeneous graph. We evaluate the model on low-grade gliomas, glioblastoma, and kidney renal papillary cell carcinoma datasets from the Cancer Genome Atlas.
  arXiv  Detail & Related papers  (2024-04-11T09:07:40Z)
• 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)
• 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)
• Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining [73.23533486979166]
  Capsular invasion on tumor boundary has proven to be clinically correlated with the prognostic indicator, microvascular invasion (MVI) In this paper, we propose the first and novel computational framework that disentangles the task into two components.
  arXiv  Detail & Related papers  (2021-03-09T01:43:05Z)

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.