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Register a new userUnsupervised Learning of Local Discriminative Representation for Medical Images
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Huai Chen
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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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.
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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.
Registration networks have shown great application potentials in medical image analysis. However, supervised training methods have a great demand for large and high-quality labeled datasets, which is time-consuming and sometimes impractical due to data sharing issues. Unsupervised image registration algorithms commonly employ intensity-based similarity measures as loss functions without any manual annotations. These methods estimate the parameterized transformations between pairs of moving and fixed images through the optimization of the network parameters during training. However, these methods become less effective when the image quality varies, e.g., some images are corrupted by substantial noise or artifacts. In this work, we propose a novel approach based on a low-rank representation, i.e., Regnet-LRR, to tackle the problem. We project noisy images into a noise-free low-rank space, and then compute the similarity between the images. Based on the low-rank similarity measure, we train the registration network to predict the dense deformation fields of noisy image pairs. We highlight that the low-rank projection is reformulated in a way that the registration network can successfully update gradients. With two tasks, i.e., cardiac and abdominal intra-modality registration, we demonstrate that the low-rank representation can boost the generalization ability and robustness of models as well as bring significant improvements in noisy data registration scenarios.
Recently deep learning has achieved significant progress on point cloud analysis tasks. Learning good representations is of vital importance to these tasks. Most current methods rely on massive labelled data for training. We here propose a point discriminative learning method for unsupervised representation learning on 3D point clouds, which can learn local and global geometry features. We achieve this by imposing a novel point discrimination loss on the middle level and global level point features produced in the backbone network. This point discrimination loss enforces the features to be consistent with points belonging to the shape surface and inconsistent with randomly sampled noisy points. Our method is simple in design, which works by adding an extra adaptation module and a point consistency module for unsupervised training of the encoder in the backbone network. Once trained, these two modules can be discarded during supervised training of the classifier or decoder for down-stream tasks. We conduct extensive experiments on 3D object classification, 3D part segmentation and shape reconstruction in various unsupervised and transfer settings. Both quantitative and qualitative results show that our method learns powerful representations and achieves new state-of-the-art performance.
Contrastive self-supervised learning has largely narrowed the gap to supervised pre-training on ImageNet. However, its success highly relies on the object-centric priors of ImageNet, i.e., different augmented views of the same image correspond to the same object. Such a heavily curated constraint becomes immediately infeasible when pre-trained on more complex scene images with many objects. To overcome this limitation, we introduce Object-level Representation Learning (ORL), a new self-supervised learning framework towards scene images. Our key insight is to leverage image-level self-supervised pre-training as the prior to discover object-level semantic correspondence, thus realizing object-level representation learning from scene images. Extensive experiments on COCO show that ORL significantly improves the performance of self-supervised learning on scene images, even surpassing supervised ImageNet pre-training on several downstream tasks. Furthermore, ORL improves the downstream performance when more unlabeled scene images are available, demonstrating its great potential of harnessing unlabeled data in the wild. We hope our approach can motivate future research on more general-purpose unsupervised representation learning from scene data. Project page: https://www.mmlab-ntu.com/project/orl/.
Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i.e., healthy) images to detect any abnormal (i.e., unhealthy) samples that do not conform to the expected normal patterns. UAD has two main advantages over its fully supervised counterpart. Firstly, it is able to directly leverage large datasets available from health screening programs that contain mostly normal image samples, avoiding the costly manual labelling of abnormal samples and the subsequent issues involved in training with extremely class-imbalanced data. Further, UAD approaches can potentially detect and localise any type of lesions that deviate from the normal patterns. One significant challenge faced by UAD methods is how to learn effective low-dimensional image representations to detect and localise subtle abnormalities, generally consisting of small lesions. To address this challenge, we propose a novel self-supervised representation learning method, called Constrained Contrastive Distribution learning for anomaly detection (CCD), which learns fine-grained feature representations by simultaneously predicting the distribution of augmented data and image contexts using contrastive learning with pretext constraints. The learned representations can be leveraged to train more anomaly-sensitive detection models. Extensive experiment results show that our method outperforms current state-of-the-art UAD approaches on three different colonoscopy and fundus screening datasets. Our code is available at https://github.com/tianyu0207/CCD.
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