A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation

• URL: http://arxiv.org/abs/2107.04934v1
• Date: Sun, 11 Jul 2021 00:40:40 GMT
• Title: A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation
• Authors: Euijoon Ahn, Dagan Feng and Jinman Kim
• Abstract summary: We propose a new spatial guided self-supervised clustering network (SGSCN) for medical image segmentation. It iteratively learns feature representations and clustering assignment of each pixel in an end-to-end fashion from a single image. We evaluated our method on 2 public medical image datasets and compared it to existing conventional and self-supervised clustering methods.
• Score: 16.448375091671004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The segmentation of medical images is a fundamental step in automated clinical decision support systems. Existing medical image segmentation methods based on supervised deep learning, however, remain problematic because of their reliance on large amounts of labelled training data. Although medical imaging data repositories continue to expand, there has not been a commensurate increase in the amount of annotated data. Hence, we propose a new spatial guided self-supervised clustering network (SGSCN) for medical image segmentation, where we introduce multiple loss functions designed to aid in grouping image pixels that are spatially connected and have similar feature representations. It iteratively learns feature representations and clustering assignment of each pixel in an end-to-end fashion from a single image. We also propose a context-based consistency loss that better delineates the shape and boundaries of image regions. It enforces all the pixels belonging to a cluster to be spatially close to the cluster centre. We evaluated our method on 2 public medical image datasets and compared it to existing conventional and self-supervised clustering methods. Experimental results show that our method was most accurate for medical image segmentation.

Related papers

• UnSegGNet: Unsupervised Image Segmentation using Graph Neural Networks [9.268228808049951]
  This research contributes to the broader field of unsupervised medical imaging and computer vision. It presents an innovative methodology for image segmentation that aligns with real-world challenges. The proposed method holds promise for diverse applications, including medical imaging, remote sensing, and object recognition.
  arXiv  Detail & Related papers  (2024-05-09T19:02:00Z)
• 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 Semantic Segmentation: Consistency over Transformation [3.485615723221064]
  We propose a novel self-supervised algorithm, textbfS$3$-Net, which integrates a robust framework based on the proposed Inception Large Kernel Attention (I-LKA) modules. We leverage deformable convolution as an integral component to effectively capture and delineate lesion deformations for superior object boundary definition. Our experimental results on skin lesion and lung organ segmentation tasks show the superior performance of our method compared to the SOTA approaches.
  arXiv  Detail & Related papers  (2023-08-31T21:28:46Z)
• 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)
• PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training [52.895952593202054]
  We propose a new semi-supervised adversarial method called Patch Confidence Adrial Training (PCA) for medical image segmentation. PCA learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state. Our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.
  arXiv  Detail & Related papers  (2022-07-24T07:45:47Z)
• Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Segmentation [46.678279106837294]
  We propose a cross-level constrastive learning scheme to enhance representation capacity for local features in semi-supervised medical image segmentation. With the help of the cross-level contrastive learning and consistency constraint, the unlabelled data can be effectively explored to improve segmentation performance.
  arXiv  Detail & Related papers  (2022-02-08T15:12:11Z)
• Contrastive Semi-Supervised Learning for 2D Medical Image Segmentation [16.517086214275654]
  We present a novel semi-supervised 2D medical segmentation solution that applies Contrastive Learning (CL) on image patches, instead of full images. These patches are meaningfully constructed using the semantic information of different classes obtained via pseudo labeling. We also propose a novel consistency regularization scheme, which works in synergy with contrastive learning.
  arXiv  Detail & Related papers  (2021-06-12T15:43:24Z)
• 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)
• Y-net: Biomedical Image Segmentation and Clustering [0.0]
  We propose a deep clustering architecture alongside image segmentation for medical image analysis. Deep clustering using Kmeans clustering is performed at the clustering branch and a lightweight model is used for segmentation. The proposed architecture can provide an early diagnosis and reduce human intervention on labeling as it can become quite costly as the datasets grow larger.
  arXiv  Detail & Related papers  (2020-04-12T21:08:31Z)
• 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)

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.