CGAM: Click-Guided Attention Module for Interactive Pathology Image Segmentation via Backpropagating Refinement

• URL: http://arxiv.org/abs/2307.01015v1
• Date: Mon, 3 Jul 2023 13:45:24 GMT
• Title: CGAM: Click-Guided Attention Module for Interactive Pathology Image Segmentation via Backpropagating Refinement
• Authors: Seonghui Min, Won-Ki Jeong
• Abstract summary: Tumor region segmentation is an essential task for the quantitative analysis of digital pathology. Recent deep neural networks have shown state-of-the-art performance in various image-segmentation tasks. We propose an interactive segmentation method that allows users to refine the output of deep neural networks through click-type user interactions.
• Score: 8.590026259176806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tumor region segmentation is an essential task for the quantitative analysis of digital pathology. Recently presented deep neural networks have shown state-of-the-art performance in various image-segmentation tasks. However, because of the unclear boundary between the cancerous and normal regions in pathology images, despite using modern methods, it is difficult to produce satisfactory segmentation results in terms of the reliability and accuracy required for medical data. In this study, we propose an interactive segmentation method that allows users to refine the output of deep neural networks through click-type user interactions. The primary method is to formulate interactive segmentation as an optimization problem that leverages both user-provided click constraints and semantic information in a feature map using a click-guided attention module (CGAM). Unlike other existing methods, CGAM avoids excessive changes in segmentation results, which can lead to the overfitting of user clicks. Another advantage of CGAM is that the model size is independent of input image size. Experimental results on pathology image datasets indicated that our method performs better than existing state-of-the-art methods.

Related papers

• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation [84.58210297703714]
  We propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. We design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting. Experiments on three medical image segmentation datasets for different tasks demonstrate the outstanding performance of our method.
  arXiv  Detail & Related papers  (2023-01-12T08:19:46Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation [4.898744396854313]
  We propose a method to synthesize for PCa histopathology images by learning the geometrical relationship between different disease labels. We use a weakly supervised segmentation approach that uses Gleason score to segment the diseased regions. The resulting segmentation map is used to train a Shape Restoration Network (ShaRe-Net) to predict missing mask segments.
  arXiv  Detail & Related papers  (2021-10-01T13:47:07Z)
• Uncertainty-Based Dynamic Graph Neighborhoods For Medical Segmentation [0.0]
  Post-processing and refining the results of segmentation is a common practice to decrease the misclassifications originating from the segmentation network. A graph-based approach makes use of certain and uncertain points in a graph and refines the segmentation according to a small graph convolutional network (GCN) We propose a new neighbor-selection mechanism according to feature distances and combine the two networks in the training procedure.
  arXiv  Detail & Related papers  (2021-08-06T13:39:35Z)
• Left Ventricle Contouring in Cardiac Images Based on Deep Reinforcement Learning [0.12891210250935145]
  We propose a new iterative refined interactive segmentation method for medical images based on agent reinforcement learning. We model the dynamic process of drawing the target contour in a certain order as a Markov Decision Process (MDP) based on a deep reinforcement learning method. Experimental results show that our method has a better segmentation effect on the left ventricle in a small number of medical image data sets.
  arXiv  Detail & Related papers  (2021-06-08T06:30:32Z)
• Learning Fuzzy Clustering for SPECT/CT Segmentation via Convolutional Neural Networks [5.3123694982708365]
  Quantitative bone single-photon emission computed tomography (QBSPECT) has the potential to provide a better quantitative assessment of bone metastasis than planar bone scintigraphy. The segmentation of anatomical regions-of-interests (ROIs) still relies heavily on the manual delineation by experts. This work proposes a fast and robust automated segmentation method for partitioning a QBSPECT image into lesion, bone, and background.
  arXiv  Detail & Related papers  (2021-04-17T19:03:52Z)
• 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)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)
• Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation [73.84166499988443]
  We present a novel unsupervised domain adaptation framework, named as Synergistic Image and Feature Alignment (SIFA) Our proposed SIFA conducts synergistic alignment of domains from both image and feature perspectives. Experimental results on two different tasks demonstrate that our SIFA method is effective in improving segmentation performance on unlabeled target images.
  arXiv  Detail & Related papers  (2020-02-06T13:49:47Z)

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.