PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training

• URL: http://arxiv.org/abs/2207.11683v1
• Date: Sun, 24 Jul 2022 07:45:47 GMT
• Title: PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training
• Authors: Zihang Xu, Zhenghua Xu, Shuo Zhang, Thomas Lukasiewicz
• Abstract summary: 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.
• Score: 52.895952593202054
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning based semi-supervised learning (SSL) methods have achieved strong performance in medical image segmentation, which can alleviate doctors' expensive annotation by utilizing a large amount of unlabeled data. Unlike most existing semi-supervised learning methods, adversarial training based methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions. We argue that the current performance restrictions for such approaches are the problems of feature extraction and learning preference. In this paper, we propose a new semi-supervised adversarial method called Patch Confidence Adversarial Training (PCA) for medical image segmentation. Rather than single scalar classification results or pixel-level confidence maps, our proposed discriminator creates patch confidence maps and classifies them at the scale of the patches. The prediction of unlabeled data 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 and improves semi-supervised segmentation performance. Furthermore, at the discriminator's input, we supplement semantic information constraints on images, making it simpler for unlabeled data to fit the expected data distribution. Extensive experiments on the Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019 challenge dataset show that our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.

Related papers

• Multi-task Explainable Skin Lesion Classification [54.76511683427566]
  We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data. The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
  arXiv  Detail & Related papers  (2023-10-11T05:49:47Z)
• Explainable Semantic Medical Image Segmentation with Style [7.074258860680265]
  We propose a fully supervised generative framework that can achieve generalisable segmentation with only limited labelled data. The proposed approach creates medical image style paired with a segmentation task driven discriminator incorporating end-to-end adversarial training. Experiments on a fully semantic, publicly available pelvis dataset demonstrated that our method is more generalisable to shifts than other state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-10T04:34:51Z)
• Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation [0.16490701092527607]
  We propose an alternative deep learning training strategy based on self-supervised pretraining on unlabeled MRI scans. Our pretraining approach first, randomly applies different distortions to random areas of unlabeled images and then predicts the type of distortions and loss of information. The effectiveness of the proposed method for segmentation tasks in different pre-training and fine-tuning scenarios is evaluated.
  arXiv  Detail & Related papers  (2022-07-17T13:28:52Z)
• Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation [9.745971699005857]
  We propose a novel uncertainty-guided mutual consistency learning framework for medical image segmentation. It integrates intra-task consistency learning from up-to-date predictions for self-ensembling and cross-task consistency learning from task-level regularization to exploit geometric shape information. Our method achieves performance gains by leveraging unlabeled data and outperforms existing semi-supervised segmentation methods.
  arXiv  Detail & Related papers  (2021-12-05T08:19:41Z)
• Positional Contrastive Learning for Volumetric Medical Image Segmentation [13.086140606803408]
  We propose a novel positional contrastive learning framework to generate contrastive data pairs. The proposed PCL method can substantially improve the segmentation performance compared to existing methods in both semi-supervised setting and transfer learning setting.
  arXiv  Detail & Related papers  (2021-06-16T22:15:28Z)
• Uncertainty guided semi-supervised segmentation of retinal layers in OCT images [4.046207281399144]
  We propose a novel uncertainty-guided semi-supervised learning based on a student-teacher approach for training the segmentation network. The proposed framework is a key contribution and applicable for biomedical image segmentation across various imaging modalities.
  arXiv  Detail & Related papers  (2021-03-02T23:14:25Z)
• Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data [123.03252888189546]
  We propose Vicinal Labels Under Uncertainty (VLUU) to bridge the methodological gaps in partially supervised learning (PSL) under data scarcity. Motivated by multi-task learning and vicinal risk minimization, VLUU transforms the partially supervised problem into a fully supervised problem by generating vicinal labels. Our research suggests a new research direction in label-efficient deep learning with partial supervision.
  arXiv  Detail & Related papers  (2020-11-28T16:31:00Z)
• Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation [54.49894381464853]
  We propose to leverage both labeled and unlabeled data for instance segmentation with improved accuracy by knowledge distillation. We propose a novel Mask-guided Mean Teacher framework with Perturbation-sensitive Sample Mining. Experiments show that the proposed method improves the performance significantly compared with the supervised method learned from labeled data only.
  arXiv  Detail & Related papers  (2020-07-21T13:27:09Z)
• ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation [99.90263375737362]
  We propose ATSO, an asynchronous version of teacher-student optimization. ATSO partitions the unlabeled data into two subsets and alternately uses one subset to fine-tune the model and updates the label on the other subset. We evaluate ATSO on two popular medical image segmentation datasets and show its superior performance in various semi-supervised settings.
  arXiv  Detail & Related papers  (2020-06-24T04:05:12Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)

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.