Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation

• URL: http://arxiv.org/abs/2301.04866v1
• Date: Thu, 12 Jan 2023 08:19:46 GMT
• Title: Self-Supervised Correction Learning for Semi-Supervised Biomedical Image Segmentation
• Authors: Ruifei Zhang, Sishuo Liu, Yizhou Yu and Guanbin Li
• Abstract summary: 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.
• Score: 84.58210297703714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Biomedical image segmentation plays a significant role in computer-aided diagnosis. However, existing CNN based methods rely heavily on massive manual annotations, which are very expensive and require huge human resources. In this work, we adopt a coarse-to-fine strategy and propose a self-supervised correction learning paradigm for semi-supervised biomedical image segmentation. Specifically, we design a dual-task network, including a shared encoder and two independent decoders for segmentation and lesion region inpainting, respectively. In the first phase, only the segmentation branch is used to obtain a relatively rough segmentation result. In the second step, we mask the detected lesion regions on the original image based on the initial segmentation map, and send it together with the original image into the network again to simultaneously perform inpainting and segmentation separately. For labeled data, this process is supervised by the segmentation annotations, and for unlabeled data, it is guided by the inpainting loss of masked lesion regions. Since the two tasks rely on similar feature information, the unlabeled data effectively enhances the representation of the network to the lesion regions and further improves the segmentation performance. Moreover, a gated feature fusion (GFF) module is designed to incorporate the complementary features from the two tasks. Experiments on three medical image segmentation datasets for different tasks including polyp, skin lesion and fundus optic disc segmentation well demonstrate the outstanding performance of our method compared with other semi-supervised approaches. The code is available at https://github.com/ReaFly/SemiMedSeg.

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)
• SamDSK: Combining Segment Anything Model with Domain-Specific Knowledge for Semi-Supervised Learning in Medical Image Segmentation [27.044797468878837]
  The Segment Anything Model (SAM) exhibits a capability to segment a wide array of objects in natural images. We propose a novel method that combines the SAM with domain-specific knowledge for reliable utilization of unlabeled images. Our work initiates a new direction of semi-supervised learning for medical image segmentation.
  arXiv  Detail & Related papers  (2023-08-26T04:46:10Z)
• Unsupervised Segmentation of Fetal Brain MRI using Deep Learning Cascaded Registration [2.494736313545503]
  Traditional deep learning-based automatic segmentation requires extensive training data with ground-truth labels. We propose a novel method based on multi-atlas segmentation, that accurately segments multiple tissues without relying on labeled data for training. Our method employs a cascaded deep learning network for 3D image registration, which computes small, incremental deformations to the moving image to align it precisely with the fixed image.
  arXiv  Detail & Related papers  (2023-07-07T13:17:12Z)
• Scale-aware Super-resolution Network with Dual Affinity Learning for Lesion Segmentation from Medical Images [50.76668288066681]
  We present a scale-aware super-resolution network to adaptively segment lesions of various sizes from low-resolution medical images. Our proposed network achieved consistent improvements compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-30T14:25:55Z)
• M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation [73.10707675345253]
  We propose a general multi-scale in multi-scale subtraction network (M$2$SNet) to finish diverse segmentation from medical image. Our method performs favorably against most state-of-the-art methods under different evaluation metrics on eleven datasets of four different medical image segmentation tasks.
  arXiv  Detail & Related papers  (2023-03-20T06:26:49Z)
• Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision [15.414578073908906]
  We propose a simple yet efficient scribble-supervised image segmentation method and apply it to cardiac MRI segmentation. By combining the scribble supervision and auxiliary pseudo labels supervision, the dual-branch network can efficiently learn from scribble annotations end-to-end.
  arXiv  Detail & Related papers  (2022-03-04T02:50:30Z)
• Cascaded Robust Learning at Imperfect Labels for Chest X-ray Segmentation [61.09321488002978]
  We present a novel cascaded robust learning framework for chest X-ray segmentation with imperfect annotation. Our model consists of three independent network, which can effectively learn useful information from the peer networks. Our methods could achieve a significant improvement on the accuracy in segmentation tasks compared to the previous methods.
  arXiv  Detail & Related papers  (2021-04-05T15:50:16Z)
• Weakly-Supervised Segmentation for Disease Localization in Chest X-Ray Images [0.0]
  We propose a novel approach to the semantic segmentation of medical chest X-ray images with only image-level class labels as supervision. We show that this approach is applicable to chest X-rays for detecting an anomalous volume of air between the lung and the chest wall.
  arXiv  Detail & Related papers  (2020-07-01T20:48:35Z)
• 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.