Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network

• URL: http://arxiv.org/abs/2207.02957v1
• Date: Wed, 6 Jul 2022 20:30:12 GMT
• Title: Context-aware Self-supervised Learning for Medical Images Using Graph Neural Network
• Authors: Li Sun, Ke Yu, Kayhan Batmanghelich
• Abstract summary: We introduce a novel approach with two levels of self-supervised representation learning objectives. We use graph neural networks to incorporate the relationship between different anatomical regions. The structure of the graph is informed by anatomical correspondences between each patient and an anatomical atlas.
• Score: 24.890564475121238
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although self-supervised learning enables us to bootstrap the training by exploiting unlabeled data, the generic self-supervised methods for natural images do not sufficiently incorporate the context. For medical images, a desirable method should be sensitive enough to detect deviation from normal-appearing tissue of each anatomical region; here, anatomy is the context. We introduce a novel approach with two levels of self-supervised representation learning objectives: one on the regional anatomical level and another on the patient-level. We use graph neural networks to incorporate the relationship between different anatomical regions. The structure of the graph is informed by anatomical correspondences between each patient and an anatomical atlas. In addition, the graph representation has the advantage of handling any arbitrarily sized image in full resolution. Experiments on large-scale Computer Tomography (CT) datasets of lung images show that our approach compares favorably to baseline methods that do not account for the context. We use the learned embedding for staging lung tissue abnormalities related to COVID-19.

Related papers

• Autoregressive Sequence Modeling for 3D Medical Image Representation [48.706230961589924]
  We introduce a pioneering method for learning 3D medical image representations through an autoregressive sequence pre-training framework. Our approach various 3D medical images based on spatial, contrast, and semantic correlations, treating them as interconnected visual tokens within a token sequence.
  arXiv  Detail & Related papers  (2024-09-13T10:19:10Z)
• Connecting the Dots: Graph Neural Network Powered Ensemble and Classification of Medical Images [0.0]
  Deep learning for medical imaging is limited due to the requirement for large amounts of training data. We employ the Image Foresting Transform to optimally segment images into superpixels. These superpixels are subsequently transformed into graph-structured data, enabling the proficient extraction of features and modeling of relationships.
  arXiv  Detail & Related papers  (2023-11-13T13:20:54Z)
• Region-based Contrastive Pretraining for Medical Image Retrieval with Anatomic Query [56.54255735943497]
  Region-based contrastive pretraining for Medical Image Retrieval (RegionMIR) We introduce a novel Region-based contrastive pretraining for Medical Image Retrieval (RegionMIR)
  arXiv  Detail & Related papers  (2023-05-09T16:46:33Z)
• 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)
• 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)
• ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics [4.907551775445731]
  We propose ContIG, a self-supervised method that can learn from large datasets of unlabeled medical images and genetic data. Our approach aligns images and several genetic modalities in the feature space using a contrastive loss. We also perform genome-wide association studies on the features learned by our models, uncovering interesting relationships between images and genetic data.
  arXiv  Detail & Related papers  (2021-11-26T11:06:12Z)
• GREN: Graph-Regularized Embedding Network for Weakly-Supervised Disease Localization in X-ray images [35.18562405272593]
  Cross-region and cross-image relationship, as contextual and compensating information, is vital to obtain more consistent and integral regions. We propose the Graph Regularized Embedding Network (GREN), which leverages the intra-image and inter-image information to locate diseases on chest X-ray images. By means of this, our approach achieves the state-of-the-art result on NIH chest X-ray dataset for weakly-supervised disease localization.
  arXiv  Detail & Related papers  (2021-07-14T01:27:07Z)
• A Survey on Graph-Based Deep Learning for Computational Histopathology [36.58189530598098]
  We have witnessed a rapid expansion of the use of machine learning and deep learning for the analysis of digital pathology and biopsy image patches. Traditional learning over patch-wise features using convolutional neural networks limits the model when attempting to capture global contextual information. We provide a conceptual grounding of graph-based deep learning and discuss its current success for tumor localization and classification, tumor invasion and staging, image retrieval, and survival prediction.
  arXiv  Detail & Related papers  (2021-07-01T07:50:35Z)
• Context Matters: Graph-based Self-supervised Representation Learning for Medical Images [21.23065972218941]
  We introduce a novel approach with two levels of self-supervised representation learning objectives. We use graph neural networks to incorporate the relationship between different anatomical regions. Our model can identify clinically relevant regions in the images.
  arXiv  Detail & Related papers  (2020-12-11T16:26:07Z)
• 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)
• Structured Landmark Detection via Topology-Adapting Deep Graph Learning [75.20602712947016]
  We present a new topology-adapting deep graph learning approach for accurate anatomical facial and medical landmark detection. The proposed method constructs graph signals leveraging both local image features and global shape features. Experiments are conducted on three public facial image datasets (WFLW, 300W, and COFW-68) as well as three real-world X-ray medical datasets (Cephalometric (public), Hand and Pelvis)
  arXiv  Detail & Related papers  (2020-04-17T11:55:03Z)

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.