Hybrid-Fusion Transformer for Multisequence MRI

• URL: http://arxiv.org/abs/2311.01308v1
• Date: Thu, 2 Nov 2023 15:22:49 GMT
• Title: Hybrid-Fusion Transformer for Multisequence MRI
• Authors: Jihoon Cho, Jinah Park
• Abstract summary: We propose the novel hybrid fusion Transformer (HFTrans) for multisequence MRI image segmentation. We take advantage of the differences among multimodal MRI sequences and utilize the Transformer layers to integrate the features extracted from each modality. We validate the effectiveness of our hybrid-fusion method in three-dimensional (3D) medical segmentation.
• Score: 2.082367820170703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical segmentation has grown exponentially through the advent of a fully convolutional network (FCN), and we have now reached a turning point through the success of Transformer. However, the different characteristics of the modality have not been fully integrated into Transformer for medical segmentation. In this work, we propose the novel hybrid fusion Transformer (HFTrans) for multisequence MRI image segmentation. We take advantage of the differences among multimodal MRI sequences and utilize the Transformer layers to integrate the features extracted from each modality as well as the features of the early fused modalities. We validate the effectiveness of our hybrid-fusion method in three-dimensional (3D) medical segmentation. Experiments on two public datasets, BraTS2020 and MRBrainS18, show that the proposed method outperforms previous state-of-the-art methods on the task of brain tumor segmentation and brain structure segmentation.

Related papers

• Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI [58.809276442508256]
  We propose a hybrid network via the combination of convolution neural network (CNN) and transformer layers. The experimental results on private and public DCE-MRI datasets demonstrate that the proposed hybrid network superior performance than the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-11T15:46:00Z)
• Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging [17.07490339960335]
  We introduce a novel hybrid CNN-Transformer segmentation architecture (PAG-TransYnet) designed for efficiently building a strong CNN-Transformer encoder. Our approach exploits attention gates within a Dual Pyramid hybrid encoder.
  arXiv  Detail & Related papers  (2024-04-28T14:37:10Z)
• SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging [59.78761085714715]
  This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework for liver lesion classification. The proposed framework has been validated through comprehensive experiments on two clinical datasets. To support the scientific community, we are releasing our extensive multi-phase MR dataset for liver lesion analysis to the public.
  arXiv  Detail & Related papers  (2024-02-27T06:32:56Z)
• Deep Multimodal Fusion of Data with Heterogeneous Dimensionality via Projective Networks [4.933439602197885]
  We propose a novel deep learning-based framework for the fusion of multimodal data with heterogeneous dimensionality (e.g., 3D+2D) The framework was validated on the following tasks: segmentation of geographic atrophy (GA), a late-stage manifestation of age-related macular degeneration, and segmentation of retinal blood vessels (RBV) in multimodal retinal imaging. Our results show that the proposed method outperforms the state-of-the-art monomodal methods on GA and RBV segmentation by up to 3.10% and 4.64% Dice, respectively.
  arXiv  Detail & Related papers  (2024-02-02T11:03:33Z)
• Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network [63.845552349914186]
  Capturing global contextual information plays a critical role in breast ultrasound (BUS) image classification. Vision Transformers have an improved capability of capturing global contextual information but may distort the local image patterns due to the tokenization operations. In this study, we proposed a hybrid multitask deep neural network called Hybrid-MT-ESTAN, designed to perform BUS tumor classification and segmentation.
  arXiv  Detail & Related papers  (2023-08-04T01:19:32Z)
• MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer [53.575573940055335]
  We propose a novel Transformer-based Diffusion framework, called MedSegDiff-V2. We verify its effectiveness on 20 medical image segmentation tasks with different image modalities.
  arXiv  Detail & Related papers  (2023-01-19T03:42:36Z)
• View-Disentangled Transformer for Brain Lesion Detection [50.4918615815066]
  We propose a novel view-disentangled transformer to enhance the extraction of MRI features for more accurate tumour detection. First, the proposed transformer harvests long-range correlation among different positions in a 3D brain scan. Second, the transformer models a stack of slice features as multiple 2D views and enhance these features view-by-view. Third, we deploy the proposed transformer module in a transformer backbone, which can effectively detect the 2D regions surrounding brain lesions.
  arXiv  Detail & Related papers  (2022-09-20T11:58:23Z)
• 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)
• mmFormer: Multimodal Medical Transformer for Incomplete Multimodal Learning of Brain Tumor Segmentation [38.22852533584288]
  We propose a novel Medical Transformer (mmFormer) for incomplete multimodal learning with three main components. The proposed mmFormer outperforms the state-of-the-art methods for incomplete multimodal brain tumor segmentation on almost all subsets of incomplete modalities.
  arXiv  Detail & Related papers  (2022-06-06T08:41:56Z)
• Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution [55.52779466954026]
  Multi-contrast super-resolution (SR) reconstruction is promising to yield SR images with higher quality. Existing methods lack effective mechanisms to match and fuse these features for better reconstruction. We propose a novel network to address these problems by developing a set of innovative Transformer-empowered multi-scale contextual matching and aggregation techniques.
  arXiv  Detail & Related papers  (2022-03-26T01:42:59Z)
• Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images [7.334185314342017]
  We propose a novel segmentation model termed Swin UNEt TRansformers (Swin UNETR) The model extracts features at five different resolutions by utilizing shifted windows for computing self-attention. We have participated in BraTS 2021 segmentation challenge, and our proposed model ranks among the top-performing approaches in the validation phase.
  arXiv  Detail & Related papers  (2022-01-04T18:01:34Z)

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.