OTCXR: Rethinking Self-supervised Alignment using Optimal Transport for Chest X-ray Analysis

• URL: http://arxiv.org/abs/2404.11868v4
• Date: Tue, 11 Mar 2025 10:09:11 GMT
• Title: OTCXR: Rethinking Self-supervised Alignment using Optimal Transport for Chest X-ray Analysis
• Authors: Vandan Gorade, Azad Singh, Deepak Mishra,
• Abstract summary: Self-supervised learning (SSL) has emerged as a promising technique for analyzing medical modalities such as X-rays.<n>We propose OTCXR, a novel SSL framework that leverages optimal transport (OT) to learn dense semantic invariance.<n>We validate OTCXR's efficacy through comprehensive experiments on three publicly available chest X-ray datasets.
• Score: 6.4136876268620115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised learning (SSL) has emerged as a promising technique for analyzing medical modalities such as X-rays due to its ability to learn without annotations. However, conventional SSL methods face challenges in achieving semantic alignment and capturing subtle details, which limits their ability to accurately represent the underlying anatomical structures and pathological features. To address these limitations, we propose OTCXR, a novel SSL framework that leverages optimal transport (OT) to learn dense semantic invariance. By integrating OT with our innovative Cross-Viewpoint Semantics Infusion Module (CV-SIM), OTCXR enhances the model's ability to capture not only local spatial features but also global contextual dependencies across different viewpoints. This approach enriches the effectiveness of SSL in the context of chest radiographs. Furthermore, OTCXR incorporates variance and covariance regularizations within the OT framework to prioritize clinically relevant information while suppressing less informative features. This ensures that the learned representations are comprehensive and discriminative, particularly beneficial for tasks such as thoracic disease diagnosis. We validate OTCXR's efficacy through comprehensive experiments on three publicly available chest X-ray datasets. Our empirical results demonstrate the superiority of OTCXR over state-of-the-art methods across all evaluated tasks, confirming its capability to learn semantically rich representations.

Related papers

• Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images [7.048241543461529]
  We propose a novel framework called Multi-Resolution Prompt-guided Hybrid Embedding (MR-PHE) to address these challenges in zero-shot histopathology image classification. We introduce a hybrid embedding strategy that integrates global image embeddings with weighted patch embeddings. A similarity-based patch weighting mechanism assigns attention-like weights to patches based on their relevance to class embeddings.
  arXiv  Detail & Related papers  (2025-03-13T12:18:37Z)
• Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding [92.32881381717594]
  We introduce ALternate Contrastive Decoding (ALCD) to solve hallucination issues in medical information extraction tasks. ALCD demonstrates significant improvements in resolving hallucination issues compared to conventional decoding methods.
  arXiv  Detail & Related papers  (2024-10-21T07:19:19Z)
• CoBooM: Codebook Guided Bootstrapping for Medical Image Representation Learning [6.838695126692698]
  Self-supervised learning has emerged as a promising paradigm for medical image analysis by harnessing unannotated data. Existing SSL approaches overlook the high anatomical similarity inherent in medical images. We propose CoBooM, a novel framework for self-supervised medical image learning by integrating continuous and discrete representations.
  arXiv  Detail & Related papers  (2024-08-08T06:59:32Z)
• MLVICX: Multi-Level Variance-Covariance Exploration for Chest X-ray Self-Supervised Representation Learning [6.4136876268620115]
  MLVICX is an approach to capture rich representations in the form of embeddings from chest X-ray images. We demonstrate the performance of MLVICX in advancing self-supervised chest X-ray representation learning.
  arXiv  Detail & Related papers  (2024-03-18T06:19:37Z)
• Overcoming Dimensional Collapse in Self-supervised Contrastive Learning for Medical Image Segmentation [2.6764957223405657]
  We investigate the application of contrastive learning to the domain of medical image analysis. Our findings reveal that MoCo v2, a state-of-the-art contrastive learning method, encounters dimensional collapse when applied to medical images. To address this, we propose two key contributions: local feature learning and feature decorrelation.
  arXiv  Detail & Related papers  (2024-02-22T15:02:13Z)
• MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning [48.97640824497327]
  We propose a novel framework leveraging domain-specific medical knowledge as guiding signals to integrate language information into the visual domain through image-text contrastive learning. Our model includes global contrastive learning with our designed divergence encoder, local token-knowledge-patch alignment contrastive learning, and knowledge-guided category-level contrastive learning with expert knowledge. Notably, MLIP surpasses state-of-the-art methods even with limited annotated data, highlighting the potential of multimodal pre-training in advancing medical representation learning.
  arXiv  Detail & Related papers  (2024-02-03T05:48:50Z)
• Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training [75.40980802817349]
  Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. We introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites.
  arXiv  Detail & Related papers  (2023-08-31T00:36:10Z)
• Revisiting Computer-Aided Tuberculosis Diagnosis [56.80999479735375]
  Tuberculosis (TB) is a major global health threat, causing millions of deaths annually. Computer-aided tuberculosis diagnosis (CTD) using deep learning has shown promise, but progress is hindered by limited training data. We establish a large-scale dataset, namely the Tuberculosis X-ray (TBX11K) dataset, which contains 11,200 chest X-ray (CXR) images with corresponding bounding box annotations for TB areas. This dataset enables the training of sophisticated detectors for high-quality CTD.
  arXiv  Detail & Related papers  (2023-07-06T08:27:48Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Anatomical Invariance Modeling and Semantic Alignment for Self-supervised Learning in 3D Medical Image Analysis [6.87667643104543]
  Self-supervised learning (SSL) has recently achieved promising performance for 3D medical image analysis tasks. Most current methods follow existing SSL paradigm originally designed for photographic or natural images. We propose a new self-supervised learning framework, namely Alice, that explicitly fulfills Anatomical invariance modeling and semantic alignment.
  arXiv  Detail & Related papers  (2023-02-11T06:36:20Z)
• PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis [56.63327669853693]
  We propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding. The proposed unified SSL framework surpasses its self-supervised counterparts on various tasks.
  arXiv  Detail & Related papers  (2023-01-02T17:47:27Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• Cross-level Contrastive Learning and Consistency Constraint for Semi-supervised Medical Image Segmentation [46.678279106837294]
  We propose a cross-level constrastive learning scheme to enhance representation capacity for local features in semi-supervised medical image segmentation. With the help of the cross-level contrastive learning and consistency constraint, the unlabelled data can be effectively explored to improve segmentation performance.
  arXiv  Detail & Related papers  (2022-02-08T15:12:11Z)
• Organ localisation using supervised and semi supervised approaches combining reinforcement learning with imitation learning [6.198237241838559]
  Computer aided diagnostics often requires analysis of a region of interest within a radiology scan. Deep learning algorithms rely on the availability of a large amount of annotated data. Motivated by the need to address this limitation, an approach to localisation and detection of multiple organs is presented.
  arXiv  Detail & Related papers  (2021-12-06T14:04:38Z)
• Variational Knowledge Distillation for Disease Classification in Chest X-Rays [102.04931207504173]
  We propose itvariational knowledge distillation (VKD), which is a new probabilistic inference framework for disease classification based on X-rays. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs.
  arXiv  Detail & Related papers  (2021-03-19T14:13:56Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• 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)
• Learning Invariant Feature Representation to Improve Generalization across Chest X-ray Datasets [55.06983249986729]
  We show that a deep learning model performing well when tested on the same dataset as training data starts to perform poorly when it is tested on a dataset from a different source. By employing an adversarial training strategy, we show that a network can be forced to learn a source-invariant representation.
  arXiv  Detail & Related papers  (2020-08-04T07:41:15Z)
• BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray dataset [6.5800499500032705]
  We design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients. We exploit a clinical dataset of almost 5,000 CXR annotated images collected in the same hospital. Our solution outperforms single human annotators in rating accuracy and consistency.
  arXiv  Detail & Related papers  (2020-06-08T13:55:58Z)
• Localization of Critical Findings in Chest X-Ray without Local Annotations Using Multi-Instance Learning [0.0]
  deep learning models commonly suffer from a lack of explainability. Deep learning models require locally annotated training data in form of pixel level labels or bounding box coordinates. In this work, we address these shortcomings with an interpretable DL algorithm based on multi-instance learning.
  arXiv  Detail & Related papers  (2020-01-23T21:29:14Z)

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.