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

• URL: http://arxiv.org/abs/2102.10499v1
• Date: Sun, 21 Feb 2021 03:38:51 GMT
• Title: A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification
• Authors: Yixin Li, Xinran Wu, Chen Li, Changhao Sun, Md Rahaman, Yudong Yao, Xiaoyan Li, Yong Zhang, Tao Jiang
• Abstract summary: In the Gastric Histopathology Image Classification (GHIC) tasks, there is inevitably redundant information in the images. This paper proposes an intelligent Hierarchical Conditional Random Field based Attention Mechanism (HCRF-AM) model. In the experiment, a classification specificity of 96.67% is achieved on a gastric histopathology dataset with 700 images.
• Score: 18.47322656765279
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the Gastric Histopathology Image Classification (GHIC) tasks, which is 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.

Related papers

• Towards a vision foundation model for comprehensive assessment of Cardiac MRI [11.838157772803282]
  We introduce a vision foundation model trained for cardiac magnetic resonance imaging (CMR) assessment. We finetune the model in supervised way for 9 clinical tasks typical to a CMR workflow. We demonstrate improved accuracy and robustness across all tasks, over a range of available labeled dataset sizes.
  arXiv  Detail & Related papers  (2024-10-02T15:32:01Z)
• Causality-Driven One-Shot Learning for Prostate Cancer Grading from MRI [1.049712834719005]
  We present a novel method to automatically classify medical images that learns and leverages weak causal signals in the image. Our framework consists of a convolutional neural network backbone and a causality-extractor module. Our findings show that causal relationships among features play a crucial role in enhancing the model's ability to discern relevant information.
  arXiv  Detail & Related papers  (2023-09-19T16:08:33Z)
• Class Attention to Regions of Lesion for Imbalanced Medical Image Recognition [59.28732531600606]
  We propose a framework named textbfClass textbfAttention to textbfREgions of the lesion (CARE) to handle data imbalance issues. The CARE framework needs bounding boxes to represent the lesion regions of rare diseases. Results show that the CARE variants with automated bounding box generation are comparable to the original CARE framework.
  arXiv  Detail & Related papers  (2023-07-19T15:19:02Z)
• Two-stage MR Image Segmentation Method for Brain Tumors based on Attention Mechanism [27.08977505280394]
  A coordination-spatial attention generation adversarial network (CASP-GAN) based on the cycle-consistent generative adversarial network (CycleGAN) is proposed. The performance of the generator is optimized by introducing the Coordinate Attention (CA) module and the Spatial Attention (SA) module. The ability to extract the structure information and the detailed information of the original medical image can help generate the desired image with higher quality.
  arXiv  Detail & Related papers  (2023-04-17T08:34:41Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image Analysis [48.02011627390706]
  We develop a novel self-supervised approach that occludes targeted regions to improve the pre-training procedure. We show that training the agent against the prediction model can significantly improve the semantic features extracted for downstream classification tasks.
  arXiv  Detail & Related papers  (2022-03-25T19:05:06Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Multi-Modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images [3.5354617056939874]
  This work presents an automatic cascade pathology segmentation framework based on multi-modality CMR images. It mainly consists of two neural networks: an anatomical structure segmentation network (ASSN) and a pathological region segmentation network (PRSN)
  arXiv  Detail & Related papers  (2020-08-13T09:57:04Z)
• Gastric histopathology image segmentation using a hierarchical conditional random field [16.920864110707747]
  A novel Conditional Random Field (HCRF) based Gastric Histopathology Image (GHIS) method is proposed. Our HCRF model demonstrates high segmentation performance and shows its effectiveness and future potential in the GHIS field.
  arXiv  Detail & Related papers  (2020-03-03T02:44:31Z)

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.