OCL: Ordinal Contrastive Learning for Imputating Features with Progressive Labels

• URL: http://arxiv.org/abs/2503.02899v1
• Date: Mon, 03 Mar 2025 07:23:29 GMT
• Title: OCL: Ordinal Contrastive Learning for Imputating Features with Progressive Labels
• Authors: Seunghun Baek, Jaeyoon Sim, Guorong Wu, Won Hwa Kim,
• Abstract summary: We introduce a holistic imaging feature imputation method that enables to leverage diverse imaging features while retaining all subjects.<n>The proposed method promotes our holistic imaging feature imputation across various modalities in the shared embedding space.<n>In the experiments, we show that our networks deliver favorable results for statistical analysis and classification against imputation baselines.
• Score: 4.434835769977399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately discriminating progressive stages of Alzheimer's Disease (AD) is crucial for early diagnosis and prevention. It often involves multiple imaging modalities to understand the complex pathology of AD, however, acquiring a complete set of images is challenging due to high cost and burden for subjects. In the end, missing data become inevitable which lead to limited sample-size and decrease in precision in downstream analyses. To tackle this challenge, we introduce a holistic imaging feature imputation method that enables to leverage diverse imaging features while retaining all subjects. The proposed method comprises two networks: 1) An encoder to extract modality-independent embeddings and 2) A decoder to reconstruct the original measures conditioned on their imaging modalities. The encoder includes a novel {\em ordinal contrastive loss}, which aligns samples in the embedding space according to the progression of AD. We also maximize modality-wise coherence of embeddings within each subject, in conjunction with domain adversarial training algorithms, to further enhance alignment between different imaging modalities. The proposed method promotes our holistic imaging feature imputation across various modalities in the shared embedding space. In the experiments, we show that our networks deliver favorable results for statistical analysis and classification against imputation baselines with Alzheimer's Disease Neuroimaging Initiative (ADNI) study.

Related papers

• Modality-Agnostic Style Transfer for Holistic Feature Imputation [5.62583732398926]
  We propose a framework that generates unobserved imaging measures for specific subjects using their existing measures.<n>Our framework transfers modality-specific style while preserving AD-specific content.<n>This is done by domain adversarial training that preserves modality-agnostic but AD-specific information, while a generative adversarial network adds an indistinguishable modality-specific style.
  arXiv  Detail & Related papers  (2025-03-03T07:09:24Z)
• Applying Conditional Generative Adversarial Networks for Imaging Diagnosis [3.881664394416534]
  This study introduces an innovative application of Conditional Generative Adversarial Networks (C-GAN) integrated with Stacked Hourglass Networks (SHGN) We address the problem of overfitting, common in deep learning models applied to complex imaging datasets, by augmenting data through rotation and scaling. A hybrid loss function combining L1 and L2 reconstruction losses, enriched with adversarial training, is introduced to refine segmentation processes in intravascular ultrasound (IVUS) imaging.
  arXiv  Detail & Related papers  (2024-07-17T23:23:09Z)
• Discrepancy-based Diffusion Models for Lesion Detection in Brain MRI [1.8420387715849447]
  Diffusion probabilistic models (DPMs) have exhibited significant effectiveness in computer vision tasks. Their notable performance heavily relies on labelled datasets, which limits their application in medical images. This paper introduces a novel framework by incorporating distinctive discrepancy features.
  arXiv  Detail & Related papers  (2024-05-08T11:26:49Z)
• QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge [93.61262892578067]
  Uncertainty in medical image segmentation tasks, especially inter-rater variability, presents a significant challenge. This variability directly impacts the development and evaluation of automated segmentation algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ)
  arXiv  Detail & Related papers  (2024-03-19T17:57:24Z)
• Diagnosing Alzheimer's Disease using Early-Late Multimodal Data Fusion with Jacobian Maps [1.5501208213584152]
  Alzheimer's disease (AD) is a prevalent and debilitating neurodegenerative disorder impacting a large aging population. We propose an efficient early-late fusion (ELF) approach, which leverages a convolutional neural network for automated feature extraction and random forests. To tackle the challenge of detecting subtle changes in brain volume, we transform images into the Jacobian domain (JD)
  arXiv  Detail & Related papers  (2023-10-25T19:02:57Z)
• MSCDA: Multi-level Semantic-guided Contrast Improves Unsupervised Domain Adaptation for Breast MRI Segmentation in Small Datasets [5.272836235045653]
  We propose a novel Multi-level Semantic-guided Contrastive Domain Adaptation framework. Our approach incorporates self-training with contrastive learning to align feature representations between domains. In particular, we extend the contrastive loss by incorporating pixel-to-pixel, pixel-to-centroid, and centroid-to-centroid contrasts.
  arXiv  Detail & Related papers  (2023-01-04T19:16:55Z)
• 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)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis [102.40869566439514]
  We seek to exploit rich labeled data from relevant domains to help the learning in the target task via Unsupervised Domain Adaptation (UDA) Unlike most UDA methods that rely on clean labeled data or assume samples are equally transferable, we innovatively propose a Collaborative Unsupervised Domain Adaptation algorithm. We theoretically analyze the generalization performance of the proposed method, and also empirically evaluate it on both medical and general images.
  arXiv  Detail & Related papers  (2020-07-05T11:49:17Z)
• Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion [71.87627318863612]
  We propose a novel multimodal segmentation framework which is robust to the absence of imaging modalities. Our network uses feature disentanglement to decompose the input modalities into the modality-specific appearance code. We validate our method on the important yet challenging multimodal brain tumor segmentation task with the BRATS challenge dataset.
  arXiv  Detail & Related papers  (2020-02-22T14:32:04Z)

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.