Semantic Segmentation-Driven Image-Level Diagnosis of Liver Cancers in Hematoxylin and Eosin Histopathology Images

• URL: http://arxiv.org/abs/2607.03253v1
• Date: Fri, 03 Jul 2026 12:12:56 GMT
• Title: Semantic Segmentation-Driven Image-Level Diagnosis of Liver Cancers in Hematoxylin and Eosin Histopathology Images
• Abstract summary: We propose a semantic segmentation-based framework for image-level diagnosis.<n>Image-level predictions are obtained by assigning the class of the dominant pixel-level label in the segmentation output.<n>Five-fold cross-validation yielded balanced accuracy of 0.975 (HCC), 0.950 (CCA), and 1.000 (CMA)
• Score: 2.4629531282150876
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As hematoxylin & eosin (H&E) staining constitutes the primary entry point in routine diagnostic workflows, computer-aided diagnosis from whole-slide H&E images is of particular clinical relevance. However, substantial variability in specimen preparation, staining protocols, and scanning conditions, together with inherent uncertainty in expert pixel-level annotations, makes automated analysis of H&E-stained images challenging. In this study, we propose a semantic segmentation-based framework for image-level diagnosis, grounded in the clinically motivated assumption that each histopathological image corresponds to a single cancer type. Image-level predictions are obtained by assigning the class of the dominant pixel-level label in the segmentation output. To ensure clinical relevance, we adopt the nnU-Net architecture and train it on a publicly available dataset collected in our study with pixel-level annotations for three liver cancer types: hepatocellular cacrcinoma (HCC; 55 images from 30 patients), cholangiocellular carcinoma (CCA; 55 images from 29 patients), and colorectal metastatic adenocarcinoma (CMA; 60 images from 30 patients). Annotations were independently provided by four pathologist. We hypothesize that the combination of stain normalization and semantic segmentation mitigates domain shift and reduces sensitivity to annotation noise. Five-fold cross-validation yielded balanced accuracy of 0.975 (HCC), 0.950 (CCA), and 1.000 (CMA), comparable to results obtained with immunohosthochemical staining and superior to several deep learning models trained on patch-level annotations. The proposed framework has the potential to support pathologists in prioritizing immunohistochemical marker selection, thereby reducing diagnostic costs and turnaround time. Integration with immunohistochemical findings improve overall diagnostic reliability.

Related papers

• Histopathological Spectrum-Guided Prostate Stratification via Segmentation-Assisted Diagnostic Transformer [29.021037203684227]
  We construct a Prostate Cancer Histopathology Spectrum dataset (PCa-HSD)<n>We formulate a clinically meaningful four-class classification task, addressing the underrepresentation of benign lesions that are easily confounded with prostate cancer in existing datasets.<n>Our proposed method consistently improves accuracy across backbones, achieving the best average accuracy of 0.633 and JointRecall of 0.768 in five-fold cross-validation on a cohort of 344 patients.
  arXiv  Detail & Related papers  (2026-07-19T10:03:47Z)
• Towards Accurate and Interpretable Neuroblastoma Diagnosis via Contrastive Multi-scale Pathological Image Analysis [16.268045905735818]
  We propose CMSwinKAN, a contrastive-learning-based multi-scale feature fusion model tailored for pathological image classification.<n>By fusing multi-scale features and leveraging contrastive learning strategies, CMSwinKAN mimics clinicians' comprehensive approach.<n>Results demonstrate that CMSwinKAN performs better than existing state-of-the-art pathology-specific models pre-trained on large datasets.
  arXiv  Detail & Related papers  (2025-04-18T15:39:46Z)
• A Classification-Based Adaptive Segmentation Pipeline: Feasibility Study Using Polycystic Liver Disease and Metastases from Colorectal Cancer CT Images [0.261201916989931]
  The purpose of this study is to explore the feasibility of building a workflow to efficiently trained segmentation models. By implementing a deep learning model to automatically classify the images and route them appropriate segmentation models, we hope our workflow can segment the images with different pathology accurately.
  arXiv  Detail & Related papers  (2024-05-02T18:05:37Z)
• Leveraging weak complementary labels to improve semantic segmentation of hepatocellular carcinoma and cholangiocarcinoma in H&E-stained slides [3.418658036508099]
  We present a deep learning segmentation approach to classify and quantify the two most prevalent primary liver cancers - carcinoma and intrahepatic cholangiocarcinoma. We propose to leverage such weak information from patient diagnoses by deriving complementary labels that indicate to which class a sample cannot belong.
  arXiv  Detail & Related papers  (2023-02-03T15:35:54Z)
• Stain-invariant self supervised learning for histopathology image analysis [74.98663573628743]
  We present a self-supervised algorithm for several classification tasks within hematoxylin and eosin stained images of breast cancer. Our method achieves the state-of-the-art performance on several publicly available breast cancer datasets.
  arXiv  Detail & Related papers  (2022-11-14T18:16:36Z)
• Lymphocyte Classification in Hyperspectral Images of Ovarian Cancer Tissue Biopsy Samples [94.37521840642141]
  We present a machine learning pipeline to segment white blood cell pixels in hyperspectral images of biopsy cores. These cells are clinically important for diagnosis, but some prior work has struggled to incorporate them due to difficulty obtaining precise pixel labels.
  arXiv  Detail & Related papers  (2022-03-23T00:58:27Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation [48.504790189796836]
  We present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe) We propose a versatile framework that fuses appearance based semi-supervision, mask based adversarial domain adaptation, and pseudo-labeling. CHASe can further improve pathological liver mask Dice-Sorensen coefficients by ranges of $4.2% sim 9.4%$.
  arXiv  Detail & Related papers  (2020-05-27T06:58:39Z)

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.