Transformer Lesion Tracker

• URL: http://arxiv.org/abs/2206.06252v1
• Date: Mon, 13 Jun 2022 15:35:24 GMT
• Title: Transformer Lesion Tracker
• Authors: Wen Tang, Han Kang, Haoyue Zhang, Pengxin Yu, Corey W. Arnold, Rongguo Zhang
• Abstract summary: We propose a transformer-based approach, termed Transformer Lesion Tracker (TLT) We design a Cross Attention-based Transformer (CAT) to capture and combine both global and local information to enhance feature extraction. We conduct experiments on a public dataset to show the superiority of our method and find that our model performance has improved the average Euclidean center error by at least 14.3%.
• Score: 12.066026343488453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Evaluating lesion progression and treatment response via longitudinal lesion tracking plays a critical role in clinical practice. Automated approaches for this task are motivated by prohibitive labor costs and time consumption when lesion matching is done manually. Previous methods typically lack the integration of local and global information. In this work, we propose a transformer-based approach, termed Transformer Lesion Tracker (TLT). Specifically, we design a Cross Attention-based Transformer (CAT) to capture and combine both global and local information to enhance feature extraction. We also develop a Registration-based Anatomical Attention Module (RAAM) to introduce anatomical information to CAT so that it can focus on useful feature knowledge. A Sparse Selection Strategy (SSS) is presented for selecting features and reducing memory footprint in Transformer training. In addition, we use a global regression to further improve model performance. We conduct experiments on a public dataset to show the superiority of our method and find that our model performance has improved the average Euclidean center error by at least 14.3% (6mm vs. 7mm) compared with the state-of-the-art (SOTA). Code is available at https://github.com/TangWen920812/TLT.

Related papers

• SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation [15.811141677039224]
  State-of-the-art methods, particularly those utilizing transformers, have been prominently adopted in 3D semantic segmentation. However, plain vision transformers encounter challenges due to their neglect of local features and their high computational complexity. We propose SegStitch, an innovative architecture that integrates transformers with denoising ODE blocks.
  arXiv  Detail & Related papers  (2024-08-01T12:05:02Z)
• A lightweight residual network for unsupervised deformable image registration [2.7309692684728617]
  We propose a residual U-Net with embedded parallel dilated-convolutional blocks to enhance the receptive field. The proposed method is evaluated on inter-patient and atlas-based datasets.
  arXiv  Detail & Related papers  (2024-06-14T07:20:49Z)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation [0.0]
  We propose a simple yet effective UNet-Transformer (seUNet-Trans) model for medical image segmentation. In our approach, the UNet model is designed as a feature extractor to generate multiple feature maps from the input images. By leveraging the UNet architecture and the self-attention mechanism, our model not only retains the preservation of both local and global context information but also is capable of capturing long-range dependencies between input elements.
  arXiv  Detail & Related papers  (2023-10-16T01:13:38Z)
• Towards Optimal Patch Size in Vision Transformers for Tumor Segmentation [2.4540404783565433]
  Detection of tumors in metastatic colorectal cancer (mCRC) plays an essential role in the early diagnosis and treatment of liver cancer. Deep learning models backboned by fully convolutional neural networks (FCNNs) have become the dominant model for segmenting 3D computerized tomography (CT) scans. Vision transformers have been introduced to solve FCNN's locality of receptive fields. This paper proposes a technique to select the vision transformer's optimal input multi-resolution image patch size based on the average volume size of metastasis lesions.
  arXiv  Detail & Related papers  (2023-08-31T09:57:27Z)
• Emergent Agentic Transformer from Chain of Hindsight Experience [96.56164427726203]
  We show that a simple transformer-based model performs competitively with both temporal-difference and imitation-learning-based approaches. This is the first time that a simple transformer-based model performs competitively with both temporal-difference and imitation-learning-based approaches.
  arXiv  Detail & Related papers  (2023-05-26T00:43:02Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• Parameter-Efficient Transformer with Hybrid Axial-Attention for Medical Image Segmentation [10.441315305453504]
  We propose a parameter-efficient transformer to explore intrinsic inductive bias via position information for medical image segmentation. Motivated by this, we present a novel Hybrid Axial-Attention (HAA) that can be equipped with spatial pixel-wise information and relative position information as inductive bias.
  arXiv  Detail & Related papers  (2022-11-17T13:54:55Z)
• Transformer-Based Source-Free Domain Adaptation [134.67078085569017]
  We study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation. We propose a generic and effective framework based on Transformer, named TransDA, for learning a generalized model for SFDA.
  arXiv  Detail & Related papers  (2021-05-28T23:06:26Z)
• Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment [60.31418655784291]
  We propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i.e., both kinematic and visual data, from simulator to real robot. It remedies the domain gap with enhanced transferable features by using temporal cues in videos, and inherent correlations in multi-modal towards recognizing gesture. Results show that our approach recovers the performance with great improvement gains, up to 12.91% in ACC and 20.16% in F1score without using any annotations in real robot.
  arXiv  Detail & Related papers  (2021-03-06T09:10:03Z)
• Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation [57.78765460295249]
  We develop a novel automatic learning-based data augmentation method for medical image segmentation. In our method, we innovatively combine the data augmentation module and the subsequent segmentation module in an end-to-end training manner with a consistent loss. We extensively evaluated our method on CT kidney tumor segmentation which validated the promising results of our method.
  arXiv  Detail & Related papers  (2020-02-22T14:10:13Z)

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.