Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation

• URL: http://arxiv.org/abs/2311.10696v2
• Date: Fri, 29 Mar 2024 20:17:29 GMT
• Title: Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation
• Authors: Xiaoyang Chen, Hao Zheng, Yuemeng Li, Yuncong Ma, Liang Ma, Hongming Li, Yong Fan,
• Abstract summary: We propose a cost-effective alternative that harnesses multi-source data with only partial or sparse segmentation labels for training. We devise strategies for model self-disambiguation, prior knowledge incorporation, and imbalance mitigation to tackle challenges associated with inconsistently labeled multi-source data.
• Score: 9.068045557591612
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A versatile medical image segmentation model applicable to images acquired with diverse equipment and protocols can facilitate model deployment and maintenance. However, building such a model typically demands a large, diverse, and fully annotated dataset, which is challenging to obtain due to the labor-intensive nature of data curation. To address this challenge, we propose a cost-effective alternative that harnesses multi-source data with only partial or sparse segmentation labels for training, substantially reducing the cost of developing a versatile model. We devise strategies for model self-disambiguation, prior knowledge incorporation, and imbalance mitigation to tackle challenges associated with inconsistently labeled multi-source data, including label ambiguity and modality, dataset, and class imbalances. Experimental results on a multi-modal dataset compiled from eight different sources for abdominal structure segmentation have demonstrated the effectiveness and superior performance of our method compared to state-of-the-art alternative approaches. We anticipate that its cost-saving features, which optimize the utilization of existing annotated data and reduce annotation efforts for new data, will have a significant impact in the field.

Related papers

• Adaptive Affinity-Based Generalization For MRI Imaging Segmentation Across Resource-Limited Settings [1.5703963908242198]
  This paper introduces a novel relation-based knowledge framework by seamlessly combining adaptive affinity-based and kernel-based distillation. To validate our innovative approach, we conducted experiments on publicly available multi-source prostate MRI data.
  arXiv  Detail & Related papers  (2024-04-03T13:35:51Z)
• Dynamic Multimodal Information Bottleneck for Multimodality Classification [26.65073424377933]
  We propose a dynamic multimodal information bottleneck framework for attaining a robust fused feature representation. Specifically, our information bottleneck module serves to filter out the task-irrelevant information and noises in the fused feature. Our method surpasses the state-of-the-art and is significantly more robust, being the only method to remain performance when large-scale noisy channels exist.
  arXiv  Detail & Related papers  (2023-11-02T08:34:08Z)
• Leveraging Diffusion Disentangled Representations to Mitigate Shortcuts in Underspecified Visual Tasks [92.32670915472099]
  We propose an ensemble diversification framework exploiting the generation of synthetic counterfactuals using Diffusion Probabilistic Models (DPMs) We show that diffusion-guided diversification can lead models to avert attention from shortcut cues, achieving ensemble diversity performance comparable to previous methods requiring additional data collection.
  arXiv  Detail & Related papers  (2023-10-03T17:37:52Z)
• Tackling Diverse Minorities in Imbalanced Classification [80.78227787608714]
  Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. We propose generating synthetic samples iteratively by mixing data samples from both minority and majority classes. We demonstrate the effectiveness of our proposed framework through extensive experiments conducted on seven publicly available benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-28T18:48:34Z)
• Learnable Weight Initialization for Volumetric Medical Image Segmentation [66.3030435676252]
  We propose a learnable weight-based hybrid medical image segmentation approach. Our approach is easy to integrate into any hybrid model and requires no external training data. Experiments on multi-organ and lung cancer segmentation tasks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-06-15T17:55:05Z)
• MultiTalent: A Multi-Dataset Approach to Medical Image Segmentation [1.146419670457951]
  Current practices limit model training and supervised pre-training to one or a few similar datasets. We propose MultiTalent, a method that leverages multiple CT datasets with diverse and conflicting class definitions.
  arXiv  Detail & Related papers  (2023-03-25T11:37:16Z)
• Effective Few-Shot Named Entity Linking by Meta-Learning [34.70028855572534]
  We propose a novel weak supervision strategy to generate non-trivial synthetic entity-mention pairs. We also design a meta-learning mechanism to assign different weights to each synthetic entity-mention pair automatically. Experiments on real-world datasets show that the proposed method can extensively improve the state-of-the-art few-shot entity linking model.
  arXiv  Detail & Related papers  (2022-07-12T03:23:02Z)
• Discriminative Multimodal Learning via Conditional Priors in Generative Models [21.166519800652047]
  This research studies the realistic scenario in which all modalities and class labels are available for model training. We show, in this scenario, that the variational lower bound limits mutual information between joint representations and missing modalities.
  arXiv  Detail & Related papers  (2021-10-09T17:22:24Z)
• Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift [68.6874404805223]
  We propose several strategies for learning from partially overlapping labels in the context of abdominal organ segmentation. We find that combining a semi-supervised approach with an adaptive cross entropy loss can successfully exploit heterogeneously annotated data.
  arXiv  Detail & Related papers  (2021-07-13T09:22:24Z)
• Accounting for Unobserved Confounding in Domain Generalization [107.0464488046289]
  This paper investigates the problem of learning robust, generalizable prediction models from a combination of datasets. Part of the challenge of learning robust models lies in the influence of unobserved confounders. We demonstrate the empirical performance of our approach on healthcare data from different modalities.
  arXiv  Detail & Related papers  (2020-07-21T08:18:06Z)
• Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models [86.9292779620645]
  We develop a contrastive framework for generative model learning, allowing us to train the model not just by the commonality between modalities, but by the distinction between "related" and "unrelated" multimodal data. Under our proposed framework, the generative model can accurately identify related samples from unrelated ones, making it possible to make use of the plentiful unlabeled, unpaired multimodal data.
  arXiv  Detail & Related papers  (2020-07-02T15:08:11Z)

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.