A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark

• URL: http://arxiv.org/abs/2203.00131v5
• Date: Wed, 5 Apr 2023 01:56:21 GMT
• Title: A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark
• Authors: Yunhe Gao, Mu Zhou, Di Liu, Zhennan Yan, Shaoting Zhang, Dimitris N. Metaxas
• Abstract summary: MedFormer is a data-scalable Transformer designed for generalizable 3D medical image segmentation. Our approach incorporates three key elements: a desirable inductive bias, hierarchical modeling with linear-complexity attention, and multi-scale feature fusion.
• Score: 45.543140413399506
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Transformers have demonstrated remarkable performance in natural language processing and computer vision. However, existing vision Transformers struggle to learn from limited medical data and are unable to generalize on diverse medical image tasks. To tackle these challenges, we present MedFormer, a data-scalable Transformer designed for generalizable 3D medical image segmentation. Our approach incorporates three key elements: a desirable inductive bias, hierarchical modeling with linear-complexity attention, and multi-scale feature fusion that integrates spatial and semantic information globally. MedFormer can learn across tiny- to large-scale data without pre-training. Comprehensive experiments demonstrate MedFormer's potential as a versatile segmentation backbone, outperforming CNNs and vision Transformers on seven public datasets covering multiple modalities (e.g., CT and MRI) and various medical targets (e.g., healthy organs, diseased tissues, and tumors). We provide public access to our models and evaluation pipeline, offering solid baselines and unbiased comparisons to advance a wide range of downstream clinical applications.

Related papers

• MedVisionLlama: Leveraging Pre-Trained Large Language Model Layers to Enhance Medical Image Segmentation [0.8437187555622164]
  This study explores enhancing Vision Transformers (ViTs) for medical image segmentation by integrating pre-trained LLM transformer blocks. Our approach, which incorporates a frozen LLM transformer block into the encoder of a ViT-based model, leads to substantial improvements in segmentation performance. The enhanced model shows significant performance gains, including an average Dice score increase from 0.74 to 0.79 and improvements in accuracy, precision, and the Jaccard Index.
  arXiv  Detail & Related papers  (2024-10-03T14:50:33Z)
• MOSMOS: Multi-organ segmentation facilitated by medical report supervision [10.396987980136602]
  We propose a novel pre-training & fine-tuning framework for Multi-Organ Supervision (MOS) Specifically, we first introduce global contrastive learning to align medical image-report pairs in the pre-training stage. To remedy the discrepancy, we further leverage multi-label recognition to implicitly learn the semantic correspondence between image pixels and organ tags.
  arXiv  Detail & Related papers  (2024-09-04T03:46:17Z)
• C^2M-DoT: Cross-modal consistent multi-view medical report generation with domain transfer network [67.97926983664676]
  We propose a cross-modal consistent multi-view medical report generation with a domain transfer network (C2M-DoT) C2M-DoT substantially outperforms state-of-the-art baselines in all metrics.
  arXiv  Detail & Related papers  (2023-10-09T02:31:36Z)
• Customizing General-Purpose Foundation Models for Medical Report Generation [64.31265734687182]
  The scarcity of labelled medical image-report pairs presents great challenges in the development of deep and large-scale neural networks. We propose customizing off-the-shelf general-purpose large-scale pre-trained models, i.e., foundation models (FMs) in computer vision and natural language processing.
  arXiv  Detail & Related papers  (2023-06-09T03:02:36Z)
• PHTrans: Parallelly Aggregating Global and Local Representations for Medical Image Segmentation [7.140322699310487]
  We propose a novel hybrid architecture for medical image segmentation called PHTrans. PHTrans parallelly hybridizes Transformer and CNN in main building blocks to produce hierarchical representations from global and local features.
  arXiv  Detail & Related papers  (2022-03-09T08:06:56Z)
• Transformers in Medical Imaging: A Survey [88.03790310594533]
  Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results. Medical imaging has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. We provide a review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues.
  arXiv  Detail & Related papers  (2022-01-24T18:50:18Z)
• TransMed: Transformers Advance Multi-modal Medical Image Classification [4.500880052705654]
  convolutional neural networks (CNN) have shown very competitive performance in medical image analysis tasks. Transformers have been applied to computer vision and achieved remarkable success in large-scale datasets. TransMed combines the advantages of CNN and transformer to efficiently extract low-level features of images.
  arXiv  Detail & Related papers  (2021-03-10T08:57:53Z)
• Medical Transformer: Gated Axial-Attention for Medical Image Segmentation [73.98974074534497]
  We study the feasibility of using Transformer-based network architectures for medical image segmentation tasks. We propose a Gated Axial-Attention model which extends the existing architectures by introducing an additional control mechanism in the self-attention module. To train the model effectively on medical images, we propose a Local-Global training strategy (LoGo) which further improves the performance.
  arXiv  Detail & Related papers  (2021-02-21T18:35:14Z)
• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation [78.01570371790669]
  Medical image segmentation is an essential prerequisite for developing healthcare systems. On various medical image segmentation tasks, the u-shaped architecture, also known as U-Net, has become the de-facto standard. We propose TransUNet, which merits both Transformers and U-Net, as a strong alternative for medical image segmentation.
  arXiv  Detail & Related papers  (2021-02-08T16:10:50Z)

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.