mlsanityData to 14 Oct 2026

A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification

arXiv 2102.10499 ↗First posted 21 Feb 2021cs.CVcited by 9

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Abstract (arXiv)

In the Gastric Histopathology Image Classification (GHIC) tasks, which are usually weakly supervised learning missions, there is inevitably redundant information in the images. Therefore, designing networks that can focus on effective distinguishing features has become a popular research topic. In this paper, to accomplish the tasks of GHIC superiorly and to assist pathologists in clinical diagnosis, an intelligent Hierarchical Conditional Random Field based Attention Mechanism (HCRF-AM) model is proposed. The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module. In the AM module, an HCRF model is built to extract attention regions. In the IC module, a Convolutional Neural Network (CNN) model is trained with the attention regions selected and then an algorithm called Classification Probability-based Ensemble Learning is applied to obtain the image-level results from patch-level output of the CNN. In the experiment, a classification specificity of 96.67% is achieved on a gastric histopathology dataset with 700 images. Our HCRF-AM model demonstrates high classification performance and shows its effectiveness and future potential in the GHIC field.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 29 of its 104 references that are in the map. First posted on arXiv 21 Feb 2021.

Builds on

29 of 104 references.

References · 29

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Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 6; 2023: 0; 2024: 0; 2025: 2.

9 papers.

  1. 2021GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detectioncited by 26
  2. 2022EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Taskscited by 15
  3. 2022IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approachcited by 7
  4. 2022Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathologycited by 6
  5. 2022TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videoscited by 2
  6. 2022A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformercited by 2
  7. 2025Unleashing Foundation Vision Models: Adaptive Transfer for Diverse Data-Limited Scientific Domainscited by 2
  8. 2022Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancercited by 0
  9. 2025CoAtNeXt:An Attention-Enhanced ConvNeXtV2-Transformer Hybrid Model for Gastric Tissue Classificationcited by 0