Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation

• URL: http://arxiv.org/abs/2403.11803v1
• Date: Mon, 18 Mar 2024 14:02:53 GMT
• Title: Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation
• Authors: Qian Dai, Dong Wei, Hong Liu, Jinghan Sun, Liansheng Wang, Yefeng Zheng,
• Abstract summary: Federated modality-specific encoders and multimodal anchors (FedMEMA) are proposed. FedMEMA employs an exclusive encoder for each modality to account for the inter-modal heterogeneity. FedMEMA is validated on the BraTS 2020 benchmark for multimodal brain tumor segmentation.
• Score: 29.584319651813754
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Most existing federated learning (FL) methods for medical image analysis only considered intramodal heterogeneity, limiting their applicability to multimodal imaging applications. In practice, it is not uncommon that some FL participants only possess a subset of the complete imaging modalities, posing inter-modal heterogeneity as a challenge to effectively training a global model on all participants' data. In addition, each participant would expect to obtain a personalized model tailored for its local data characteristics from the FL in such a scenario. In this work, we propose a new FL framework with federated modality-specific encoders and multimodal anchors (FedMEMA) to simultaneously address the two concurrent issues. Above all, FedMEMA employs an exclusive encoder for each modality to account for the inter-modal heterogeneity in the first place. In the meantime, while the encoders are shared by the participants, the decoders are personalized to meet individual needs. Specifically, a server with full-modal data employs a fusion decoder to aggregate and fuse representations from all modality-specific encoders, thus bridging the modalities to optimize the encoders via backpropagation reversely. Meanwhile, multiple anchors are extracted from the fused multimodal representations and distributed to the clients in addition to the encoder parameters. On the other end, the clients with incomplete modalities calibrate their missing-modal representations toward the global full-modal anchors via scaled dot-product cross-attention, making up the information loss due to absent modalities while adapting the representations of present ones. FedMEMA is validated on the BraTS 2020 benchmark for multimodal brain tumor segmentation. Results show that it outperforms various up-to-date methods for multimodal and personalized FL and that its novel designs are effective. Our code is available.

Related papers

• MIND: Modality-Informed Knowledge Distillation Framework for Multimodal Clinical Prediction Tasks [50.98856172702256]
  We propose the Modality-INformed knowledge Distillation (MIND) framework, a multimodal model compression approach. MIND transfers knowledge from ensembles of pre-trained deep neural networks of varying sizes into a smaller multimodal student. We evaluate MIND on binary and multilabel clinical prediction tasks using time series data and chest X-ray images.
  arXiv  Detail & Related papers  (2025-02-03T08:50:00Z)
• AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation [2.8498944632323755]
  In clinical practice, full imaging is not always feasible, often due to complex acquisition protocols, stringent privacy regulations, or specific clinical needs. A promising solution is missing data imputation, where absent modalities are generated from available ones. We propose an Adaptive Multi-Modality Diffusion Network (AMM-Diff), a novel diffusion-based generative model capable of handling any number of input modalities and generating the missing ones.
  arXiv  Detail & Related papers  (2025-01-22T12:29:33Z)
• Spectrum-based Modality Representation Fusion Graph Convolutional Network for Multimodal Recommendation [7.627299398469962]
  We propose a new Spectrum-based Modality Representation graph recommender. It aims to capture both uni-modal and fusion preferences while simultaneously suppressing modality noise. Experiments on three real-world datasets show the efficacy of our proposed model.
  arXiv  Detail & Related papers  (2024-12-19T15:53:21Z)
• StitchFusion: Weaving Any Visual Modalities to Enhance Multimodal Semantic Segmentation [63.31007867379312]
  We propose StitchFusion, a framework that integrates large-scale pre-trained models directly as encoders and feature fusers. We introduce a multi-directional adapter module (MultiAdapter) to enable cross-modal information transfer during encoding. Our model achieves state-of-the-art performance on four multi-modal segmentation datasets with minimal additional parameters.
  arXiv  Detail & Related papers  (2024-08-02T15:41:16Z)
• U3M: Unbiased Multiscale Modal Fusion Model for Multimodal Semantic Segmentation [63.31007867379312]
  We introduce U3M: An Unbiased Multiscale Modal Fusion Model for Multimodal Semantics. We employ feature fusion at multiple scales to ensure the effective extraction and integration of both global and local features. Experimental results demonstrate that our approach achieves superior performance across multiple datasets.
  arXiv  Detail & Related papers  (2024-05-24T08:58:48Z)
• All in One Framework for Multimodal Re-identification in the Wild [58.380708329455466]
  multimodal learning paradigm for ReID introduced, referred to as All-in-One (AIO) AIO harnesses a frozen pre-trained big model as an encoder, enabling effective multimodal retrieval without additional fine-tuning. Experiments on cross-modal and multimodal ReID reveal that AIO not only adeptly handles various modal data but also excels in challenging contexts.
  arXiv  Detail & Related papers  (2024-05-08T01:04:36Z)
• Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation [51.80447197290866]
  Multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given knowledge graphs. Existing MMKGC methods usually extract multi-modal features with pre-trained models. We introduce a novel framework MyGO to tokenize, fuse, and augment the fine-grained multi-modal representations of entities.
  arXiv  Detail & Related papers  (2024-04-15T05:40:41Z)
• Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction [26.994070472726357]
  Fed-PMG is a novel communication-efficient federated learning framework. We propose a pseudo modality generation mechanism to recover the missing modality for each single-modal client. Our approach can effectively complete the missing modality within an acceptable communication cost.
  arXiv  Detail & Related papers  (2023-08-20T03:38:59Z)
• Unimodal Training-Multimodal Prediction: Cross-modal Federated Learning with Hierarchical Aggregation [16.308470947384134]
  HA-Fedformer is a novel transformer-based model that empowers unimodal training with only a unimodal dataset at the client. We develop an uncertainty-aware aggregation method for the local encoders with layer-wise Markov Chain Monte Carlo sampling. Our experiments on popular sentiment analysis benchmarks, CMU-MOSI and CMU-MOSEI, demonstrate that HA-Fedformer significantly outperforms state-of-the-art multimodal models.
  arXiv  Detail & Related papers  (2023-03-27T07:07:33Z)
• NestedFormer: Nested Modality-Aware Transformer for Brain Tumor Segmentation [29.157465321864265]
  We propose a novel Nested Modality-Aware Transformer (NestedFormer) to explore the intra-modality and inter-modality relationships of multi-modal MRIs for brain tumor segmentation. Built on the transformer-based multi-encoder and single-decoder structure, we perform nested multi-modal fusion for high-level representations of different modalities.
  arXiv  Detail & Related papers  (2022-08-31T14:04:25Z)
• Attention Bottlenecks for Multimodal Fusion [90.75885715478054]
  Machine perception models are typically modality-specific and optimised for unimodal benchmarks. We introduce a novel transformer based architecture that uses fusion' for modality fusion at multiple layers. We conduct thorough ablation studies, and achieve state-of-the-art results on multiple audio-visual classification benchmarks.
  arXiv  Detail & Related papers  (2021-06-30T22:44:12Z)

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.