Feasibility and benefits of joint learning from MRI databases with different brain diseases and modalities for segmentation

• URL: http://arxiv.org/abs/2405.18511v1
• Date: Tue, 28 May 2024 18:28:10 GMT
• Title: Feasibility and benefits of joint learning from MRI databases with different brain diseases and modalities for segmentation
• Authors: Wentian Xu, Matthew Moffat, Thalia Seale, Ziyun Liang, Felix Wagner, Daniel Whitehouse, David Menon, Virginia Newcombe, Natalie Voets, Abhirup Banerjee, Konstantinos Kamnitsas,
• Abstract summary: We show that joint training on multi-modal MRI databases with different brain pathologies and sets of modalities is feasible and offers practical benefits. It enables a single model to segment pathologies encountered during training in diverse sets of modalities.
• Score: 2.7575121770012503
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Models for segmentation of brain lesions in multi-modal MRI are commonly trained for a specific pathology using a single database with a predefined set of MRI modalities, determined by a protocol for the specific disease. This work explores the following open questions: Is it feasible to train a model using multiple databases that contain varying sets of MRI modalities and annotations for different brain pathologies? Will this joint learning benefit performance on the sets of modalities and pathologies available during training? Will it enable analysis of new databases with different sets of modalities and pathologies? We develop and compare different methods and show that promising results can be achieved with appropriate, simple and practical alterations to the model and training framework. We experiment with 7 databases containing 5 types of brain pathologies and different sets of MRI modalities. Results demonstrate, for the first time, that joint training on multi-modal MRI databases with different brain pathologies and sets of modalities is feasible and offers practical benefits. It enables a single model to segment pathologies encountered during training in diverse sets of modalities, while facilitating segmentation of new types of pathologies such as via follow-up fine-tuning. The insights this study provides into the potential and limitations of this paradigm should prove useful for guiding future advances in the direction. Code and pretrained models: https://github.com/WenTXuL/MultiUnet

Related papers

• Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities [5.629645463085369]
  Models for brain lesions in MRI are typically developed for a specific disease and trained on data with a predefined set of MRI modalities. This training paradigm prevents a model from using the advantages of learning from heterogeneous databases. We demonstrate promising results by combining appropriate, simple, and practical modifications to the model and training strategy.
  arXiv  Detail & Related papers  (2024-06-17T15:16:18Z)
• Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal Dependency [31.047259264831947]
  A common problem in practice is the unavailability of some modalities due to varying scanning protocols and patient conditions. Previous methods have attempted to address this by fusing accessible multi-modal features, leveraging attention mechanisms, and synthesizing missing modalities. We propose a novel approach that enhances the deep learning-based brain tumor segmentation model from two perspectives.
  arXiv  Detail & Related papers  (2024-06-14T16:54:53Z)
• MindFormer: Semantic Alignment of Multi-Subject fMRI for Brain Decoding [50.55024115943266]
  We introduce a novel semantic alignment method of multi-subject fMRI signals using so-called MindFormer. This model is specifically designed to generate fMRI-conditioned feature vectors that can be used for conditioning Stable Diffusion model for fMRI- to-image generation or large language model (LLM) for fMRI-to-text generation. Our experimental results demonstrate that MindFormer generates semantically consistent images and text across different subjects.
  arXiv  Detail & Related papers  (2024-05-28T00:36:25Z)
• A Foundation Model for Brain Lesion Segmentation with Mixture of Modality Experts [3.208907282505264]
  We propose a universal foundation model for 3D brain lesion segmentation. We formulate a novel Mixture of Modality Experts (MoME) framework with multiple expert networks attending to different imaging modalities. Our model outperforms state-of-the-art universal models and provides promising generalization to unseen datasets.
  arXiv  Detail & Related papers  (2024-05-16T16:49:20Z)
• Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
  We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
  arXiv  Detail & Related papers  (2023-05-25T16:29:16Z)
• DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans [16.93394669748461]
  Brain tumor segmentation based on multi-modal magnetic resonance imaging (MRI) plays a pivotal role in assisting brain cancer diagnosis, treatment, and postoperative evaluations. Despite the achieved inspiring performance by existing automatic segmentation methods, multi-modal MRI data are still unavailable in real-world clinical applications. We propose a Deeply supervIsed knowledGE tranSfer neTwork (DIGEST), which achieves accurate brain tumor segmentation under different modality-missing scenarios.
  arXiv  Detail & Related papers  (2022-11-15T09:01:14Z)
• Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions [66.40971096248946]
  In this paper, we collect a series of MedISeg tricks for different model implementation phases. We experimentally explore the effectiveness of these tricks on consistent baselines. We also open-sourced a strong MedISeg repository, where each component has the advantage of plug-and-play.
  arXiv  Detail & Related papers  (2022-09-21T12:30:05Z)
• MMLN: Leveraging Domain Knowledge for Multimodal Diagnosis [10.133715767542386]
  We propose a knowledge-driven and data-driven framework for lung disease diagnosis. We formulate diagnosis rules according to authoritative clinical medicine guidelines and learn the weights of rules from text data. A multimodal fusion consisting of text and image data is designed to infer the marginal probability of lung disease.
  arXiv  Detail & Related papers  (2022-02-09T04:12:30Z)
• Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
  This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
  arXiv  Detail & Related papers  (2022-01-07T07:46:01Z)
• Modality Completion via Gaussian Process Prior Variational Autoencoders for Multi-Modal Glioma Segmentation [75.58395328700821]
  We propose a novel model, Multi-modal Gaussian Process Prior Variational Autoencoder (MGP-VAE), to impute one or more missing sub-modalities for a patient scan. MGP-VAE can leverage the Gaussian Process (GP) prior on the Variational Autoencoder (VAE) to utilize the subjects/patients and sub-modalities correlations. We show the applicability of MGP-VAE on brain tumor segmentation where either, two, or three of four sub-modalities may be missing.
  arXiv  Detail & Related papers  (2021-07-07T19:06:34Z)
• Cross-Modal Information Maximization for Medical Imaging: CMIM [62.28852442561818]
  In hospitals, data are siloed to specific information systems that make the same information available under different modalities. This offers unique opportunities to obtain and use at train-time those multiple views of the same information that might not always be available at test-time. We propose an innovative framework that makes the most of available data by learning good representations of a multi-modal input that are resilient to modality dropping at test-time.
  arXiv  Detail & Related papers  (2020-10-20T20:05:35Z)

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.