Semi-Supervised Medical Image Segmentation with Co-Distribution Alignment

• URL: http://arxiv.org/abs/2307.12630v1
• Date: Mon, 24 Jul 2023 09:08:30 GMT
• Title: Semi-Supervised Medical Image Segmentation with Co-Distribution Alignment
• Authors: Tao Wang, Zhongzheng Huang, Jiawei Wu, Yuanzheng Cai, Zuoyong Li
• Abstract summary: This paper proposes Co-Distribution Alignment (Co-DA) for semi-supervised medical image segmentation. Co-DA aligns marginal predictions on unlabeled data to marginal predictions on labeled data in a class-wise manner. We show that the proposed approach outperforms existing state-of-the-art semi-supervised medical image segmentation methods.
• Score: 16.038016822861092
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical image segmentation has made significant progress when a large amount of labeled data are available. However, annotating medical image segmentation datasets is expensive due to the requirement of professional skills. Additionally, classes are often unevenly distributed in medical images, which severely affects the classification performance on minority classes. To address these problems, this paper proposes Co-Distribution Alignment (Co-DA) for semi-supervised medical image segmentation. Specifically, Co-DA aligns marginal predictions on unlabeled data to marginal predictions on labeled data in a class-wise manner with two differently initialized models before using the pseudo-labels generated by one model to supervise the other. Besides, we design an over-expectation cross-entropy loss for filtering the unlabeled pixels to reduce noise in their pseudo-labels. Quantitative and qualitative experiments on three public datasets demonstrate that the proposed approach outperforms existing state-of-the-art semi-supervised medical image segmentation methods on both the 2D CaDIS dataset and the 3D LGE-MRI and ACDC datasets, achieving an mIoU of 0.8515 with only 24% labeled data on CaDIS, and a Dice score of 0.8824 and 0.8773 with only 20% data on LGE-MRI and ACDC, respectively.

Related papers

• Fair Text to Medical Image Diffusion Model with Subgroup Distribution Aligned Tuning [12.064840522920251]
  The text to medical image (T2MedI) with latent diffusion model has great potential to alleviate the scarcity of medical imaging data. However, as the text to nature image models, we show that the T2MedI model can also bias to some subgroups to overlook the minority ones in the training set. In this work, we first build a T2MedI model based on the pre-trained Imagen model, which has the fixed contrastive language-image pre-training (CLIP) text encoder. Its decoder has been fine-tuned on medical images from the Radiology Objects in C
  arXiv  Detail & Related papers  (2024-06-21T03:23:37Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels [54.58539616385138]
  We introduce a novel semi-supervised 2D medical image segmentation framework termed Mine yOur owN Anatomy (MONA) First, prior work argues that every pixel equally matters to the model training; we observe empirically that this alone is unlikely to define meaningful anatomical features. Second, we construct a set of objectives that encourage the model to be capable of decomposing medical images into a collection of anatomical features.
  arXiv  Detail & Related papers  (2022-09-27T15:50:31Z)
• 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)
• Self-Supervised Learning as a Means To Reduce the Need for Labeled Data in Medical Image Analysis [64.4093648042484]
  We use a dataset of chest X-ray images with bounding box labels for 13 different classes of anomalies. We show that it is possible to achieve similar performance to a fully supervised model in terms of mean average precision and accuracy with only 60% of the labeled data.
  arXiv  Detail & Related papers  (2022-06-01T09:20:30Z)
• PoissonSeg: Semi-Supervised Few-Shot Medical Image Segmentation via Poisson Learning [0.505645669728935]
  Few-shot Semantic (FSS) is a promising strategy for breaking the deadlock in deep learning. FSS model still requires sufficient pixel-level annotated classes for training to avoid overfitting. We propose a novel semi-supervised FSS framework for medical image segmentation.
  arXiv  Detail & Related papers  (2021-08-26T10:24:04Z)
• Self-Paced Contrastive Learning for Semi-supervisedMedical Image Segmentation with Meta-labels [6.349708371894538]
  We propose to adapt contrastive learning to work with meta-label annotations. We use the meta-labels for pre-training the image encoder as well as to regularize a semi-supervised training. Results on three different medical image segmentation datasets show that our approach highly boosts the performance of a model trained on a few scans.
  arXiv  Detail & Related papers  (2021-07-29T04:30:46Z)
• Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentation [40.41161371507547]
  We develop a data augmentation method for one-shot brain magnetic resonance imaging (MRI) image segmentation. Our method exploits only one labeled MRI image (named atlas) and a few unlabeled images. Our method outperforms the state-of-the-art one-shot medical segmentation methods.
  arXiv  Detail & Related papers  (2021-02-03T12:28:04Z)
• Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation [35.33425093398756]
  Unlabeled data is much easier to acquire than well-annotated data. We propose uncertainty-aware multi-view co-training for medical image segmentation. Our framework is capable of efficiently utilizing unlabeled data for better performance.
  arXiv  Detail & Related papers  (2020-06-28T22:04:54Z)
• 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.