Unsupervised Local Discrimination for Medical Images

• URL: http://arxiv.org/abs/2108.09440v1
• Date: Sat, 21 Aug 2021 04:53:19 GMT
• Title: Unsupervised Local Discrimination for Medical Images
• Authors: Huai Chen, Renzhen Wang, Jieyu Li, Qing Peng, Deyu Meng and Lisheng Wang
• Abstract summary: Contrastive representation learning is an effective method to alleviate the demand for expensive annotated data in medical image processing. Recent work mainly based on instance-wise discrimination to learn global features, while neglect local details. We propose a universal local discrmination framework to learn local discriminative features to effectively initialize medical models.
• Score: 35.14445357879895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrastive representation learning is an effective unsupervised method to alleviate the demand for expensive annotated data in medical image processing. Recent work mainly based on instance-wise discrimination to learn global features, while neglect local details, which limit their application in processing tiny anatomical structures, tissues and lesions. Therefore, we aim to propose a universal local discrmination framework to learn local discriminative features to effectively initialize medical models, meanwhile, we systematacially investigate its practical medical applications. Specifically, based on the common property of intra-modality structure similarity, i.e. similar structures are shared among the same modality images, a systematic local feature learning framework is proposed. Instead of making instance-wise comparisons based on global embedding, our method makes pixel-wise embedding and focuses on measuring similarity among patches and regions. The finer contrastive rule makes the learnt representation more generalized for segmentation tasks and outperform extensive state-of-the-art methods by wining 11 out of all 12 downstream tasks in color fundus and chest X-ray. Furthermore, based on the property of inter-modality shape similarity, i.e. structures may share similar shape although in different medical modalities, we joint across-modality shape prior into region discrimination to realize unsupervised segmentation. It shows the feaibility of segmenting target only based on shape description from other modalities and inner pattern similarity provided by region discrimination. Finally, we enhance the center-sensitive ability of patch discrimination by introducing center-sensitive averaging to realize one-shot landmark localization, this is an effective application for patch discrimination.

Related papers

• 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)
• The Role of Local Alignment and Uniformity in Image-Text Contrastive Learning on Medical Images [7.49320945341034]
  We study how local contrastive losses are related to global (per-sample) contrastive losses and which effects they have on localized medical downstream tasks. Based on a theoretical comparison, we propose to remove some components of local losses and replace others by a novel distribution prior. We empirically study this approach on chest X-ray tasks and find it to be very effective, outperforming methods without local losses on 12 of 18 tasks.
  arXiv  Detail & Related papers  (2022-11-14T10:32:51Z)
• 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)
• ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classification [11.680355561258427]
  High-resolution images hinder progress in digital pathology. patch-based processing often incorporates multiple instance learning (MIL) to aggregate local patch-level representations yielding image-level prediction. This paper proposes a transformer-based architecture specifically tailored for histological image classification. It combines fine-grained local attention with a coarse global attention mechanism to learn meaningful representations of high-resolution images at an efficient computational cost.
  arXiv  Detail & Related papers  (2022-02-15T16:55:09Z)
• A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation [16.448375091671004]
  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.
  arXiv  Detail & Related papers  (2021-07-11T00:40:40Z)
• Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
  In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme. Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor. The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
  arXiv  Detail & Related papers  (2021-05-17T10:11:45Z)
• Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation [51.93711960601973]
  We propose a novel margin preserving self-paced contrastive Learning model for cross-modal medical image segmentation. With the guidance of progressively refined semantic prototypes, a novel margin preserving contrastive loss is proposed to boost the discriminability of embedded representation space. Experiments on cross-modal cardiac segmentation tasks demonstrate that MPSCL significantly improves semantic segmentation performance.
  arXiv  Detail & Related papers  (2021-03-15T15:23:10Z)
• Unsupervised Learning of Local Discriminative Representation for Medical Images [32.155071351332964]
  Local discriminative representation is needed in many medical image analysis tasks. In this work, we introduce local discrimination into unsupervised representation learning. The effectiveness and usefulness of the proposed method are demonstrated.
  arXiv  Detail & Related papers  (2020-12-17T00:20:23Z)
• 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)
• Thoracic Disease Identification and Localization using Distance Learning and Region Verification [15.851040584898463]
  We propose an alternative approach that learns discriminative features among triplets of images and cyclically trains on region features to verify whether attentive regions contain information indicative of a disease. Our model can achieve state-of-the-art classification performance on the challenging ChestX-ray14 dataset, and our ablation studies indicate that both distance learning and region verification contribute to overall classification performance.
  arXiv  Detail & Related papers  (2020-06-07T16:56:50Z)

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.