Unsupervised Learning of Local Discriminative Representation for Medical Images

• URL: http://arxiv.org/abs/2012.09333v2
• Date: Wed, 24 Mar 2021 12:30:47 GMT
• Title: Unsupervised Learning of Local Discriminative Representation for Medical Images
• Authors: Huai Chen, Jieyu Li, Renzhen Wang, Yijie Huang, Fanrui Meng, Deyu Meng, Qing Peng, Lisheng Wang
• Abstract summary: 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.
• Score: 32.155071351332964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Local discriminative representation is needed in many medical image analysis tasks such as identifying sub-types of lesion or segmenting detailed components of anatomical structures. However, the commonly applied supervised representation learning methods require a large amount of annotated data, and unsupervised discriminative representation learning distinguishes different images by learning a global feature, both of which are not suitable for localized medical image analysis tasks. In order to avoid the limitations of these two methods, we introduce local discrimination into unsupervised representation learning in this work. The model contains two branches: one is an embedding branch which learns an embedding function to disperse dissimilar pixels over a low-dimensional hypersphere; and the other is a clustering branch which learns a clustering function to classify similar pixels into the same cluster. These two branches are trained simultaneously in a mutually beneficial pattern, and the learnt local discriminative representations are able to well measure the similarity of local image regions. These representations can be transferred to enhance various downstream tasks. Meanwhile, they can also be applied to cluster anatomical structures from unlabeled medical images under the guidance of topological priors from simulation or other structures with similar topological characteristics. The effectiveness and usefulness of the proposed method are demonstrated by enhancing various downstream tasks and clustering anatomical structures in retinal images and chest X-ray images.

Related papers

• Bayesian Unsupervised Disentanglement of Anatomy and Geometry for Deep Groupwise Image Registration [50.62725807357586]
  This article presents a general Bayesian learning framework for multi-modal groupwise image registration. We propose a novel hierarchical variational auto-encoding architecture to realise the inference procedure of the latent variables. Experiments were conducted to validate the proposed framework, including four different datasets from cardiac, brain, and abdominal medical images.
  arXiv  Detail & Related papers  (2024-01-04T08:46:39Z)
• 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)
• 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)
• Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification [52.94051907952536]
  We propose embedding metric learning into the first stage of the two-stage framework specially to help the feature extractor learn to extract more discriminative feature representations. Experiments mainly on three medical image datasets show that the proposed approach consistently outperforms existing onestage and two-stage approaches.
  arXiv  Detail & Related papers  (2022-07-14T14:57:01Z)
• Stain based contrastive co-training for histopathological image analysis [61.87751502143719]
  We propose a novel semi-supervised learning approach for classification of histovolution images. We employ strong supervision with patch-level annotations combined with a novel co-training loss to create a semi-supervised learning framework. We evaluate our approach in clear cell renal cell and prostate carcinomas, and demonstrate improvement over state-of-the-art semi-supervised learning methods.
  arXiv  Detail & Related papers  (2022-06-24T22:25:31Z)
• Leveraging Uncertainty for Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images [25.429124017422385]
  Deep weakly supervised methods allow image classification and ROI segmentation for interpretability. These methods lack mechanisms for modeling explicitly non-discriminative regions which raises false-positive rates. We propose novel regularization terms, which enable the model to seek both non-discriminative and discriminative regions.
  arXiv  Detail & Related papers  (2022-05-12T02:18:23Z)
• Unsupervised Local Discrimination for Medical Images [35.14445357879895]
  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.
  arXiv  Detail & Related papers  (2021-08-21T04:53:19Z)
• 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)
• 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)
• Orientation-Disentangled Unsupervised Representation Learning for Computational Pathology [6.468635277309852]
  We propose to extend the Variational Auto-Encoder framework by leveraging the group structure of rotation-equivariant convolutional networks. We show that the trained models efficiently disentangle the inherent orientation information of single-cell images.
  arXiv  Detail & Related papers  (2020-08-26T16:57:45Z)
• Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning [13.30545860115548]
  We design a robust and flexible model for cross-stained segmentation. It is able to improve segmentation performance on target type of stained images and use unlabeled data to achieve similar accuracy to labeled data.
  arXiv  Detail & Related papers  (2020-02-20T06:49:48Z)

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.