InstantGroup: Instant Template Generation for Scalable Group of Brain MRI Registration

• URL: http://arxiv.org/abs/2211.05622v2
• Date: Wed, 26 Jun 2024 15:34:47 GMT
• Title: InstantGroup: Instant Template Generation for Scalable Group of Brain MRI Registration
• Authors: Ziyi He, Albert C. S. Chung,
• Abstract summary: We present InstantGroup, an efficient groupwise template generation framework based on variational autoencoder (VAE) models. Experiments on 3D brain MRI scans reveal that InstantGroup dramatically reduces runtime, generating templates within seconds for various group sizes.
• Score: 5.361571536184391
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Template generation is a critical step in groupwise image registration, which involves aligning a group of subjects into a common space. While existing methods can generate high-quality template images, they often incur substantial time costs or are limited by fixed group scales. In this paper, we present InstantGroup, an efficient groupwise template generation framework based on variational autoencoder (VAE) models that leverage latent representations' arithmetic properties, enabling scalability to groups of any size. InstantGroup features a Dual VAEs backbone with shared-weight twin networks to handle pairs of inputs and incorporates a Displacement Inversion Module (DIM) to maintain template unbiasedness and a Subject-Template Alignment Module (STAM) to improve template quality and registration accuracy. Experiments on 3D brain MRI scans from the OASIS and ADNI datasets reveal that InstantGroup dramatically reduces runtime, generating templates within seconds for various group sizes while maintaining superior performance compared to state-of-the-art baselines on quantitative metrics, including unbiasedness and registration accuracy.

Related papers

• A Plug-and-Play Method for Rare Human-Object Interactions Detection by Bridging Domain Gap [50.079224604394]
  We present a novel model-agnostic framework called textbfContext-textbfEnhanced textbfFeature textbfAment (CEFA) CEFA consists of a feature alignment module and a context enhancement module. Our method can serve as a plug-and-play module to improve the detection performance of HOI models on rare categories.
  arXiv  Detail & Related papers  (2024-07-31T08:42:48Z)
• GroupMamba: Parameter-Efficient and Accurate Group Visual State Space Model [66.35608254724566]
  State-space models (SSMs) have showcased effective performance in modeling long-range dependencies with subquadratic complexity. However, pure SSM-based models still face challenges related to stability and achieving optimal performance on computer vision tasks. Our paper addresses the challenges of scaling SSM-based models for computer vision, particularly the instability and inefficiency of large model sizes.
  arXiv  Detail & Related papers  (2024-07-18T17:59:58Z)
• Advancing Vision Transformers with Group-Mix Attention [59.585623293856735]
  Group-Mix Attention (GMA) is an advanced replacement for traditional self-attention. GMA simultaneously captures token-to-token, token-to-group, and group-to-group correlations with various group sizes. GroupMixFormer achieves state-of-the-art performance in image classification, object detection, and semantic segmentation.
  arXiv  Detail & Related papers  (2023-11-26T01:25:03Z)
• Sequential Data Generation with Groupwise Diffusion Process [24.664800903343462]
  Groupwise Diffusion Model (GDM) generates data sequentially from one group at one time interval. As an extension of diffusion models, GDM generalizes certain forms of autoregressive models and cascaded diffusion models.
  arXiv  Detail & Related papers  (2023-10-02T17:58:47Z)
• GroupFormer: Group Activity Recognition with Clustered Spatial-Temporal Transformer [16.988878921451484]
  GroupFormer captures spatial-temporal contextual information jointly to augment the individual and group representations. The proposed framework outperforms state-of-the-art methods on the Volleyball dataset and Collective Activity dataset.
  arXiv  Detail & Related papers  (2021-08-28T11:24:36Z)
• Learning Multi-Attention Context Graph for Group-Based Re-Identification [214.84551361855443]
  Learning to re-identify or retrieve a group of people across non-overlapped camera systems has important applications in video surveillance. In this work, we consider employing context information for identifying groups of people, i.e., group re-id. We propose a novel unified framework based on graph neural networks to simultaneously address the group-based re-id tasks.
  arXiv  Detail & Related papers  (2021-04-29T09:57:47Z)
• Group Communication with Context Codec for Ultra-Lightweight Source Separation [32.975741399690214]
  We propose the group communication with context (GC3) design to decrease both model size and complexity without sacrificing the model performance. GC3 can achieve on par or better performance than a wide range of baseline architectures with as small as 2.5% model size.
  arXiv  Detail & Related papers  (2020-12-14T06:57:58Z)
• CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection [91.91911418421086]
  Co-Salient Object Detection (CoSOD) aims at discovering salient objects that repeatedly appear in a given query group containing two or more relevant images. One challenging issue is how to effectively capture co-saliency cues by modeling and exploiting inter-image relationships. We present an end-to-end collaborative aggregation-and-distribution network (CoADNet) to capture both salient and repetitive visual patterns from multiple images.
  arXiv  Detail & Related papers  (2020-11-10T04:28:11Z)
• Model Patching: Closing the Subgroup Performance Gap with Data Augmentation [50.35010342284508]
  We introduce model patching, a framework for improving robustness of machine learning models. Model patching encourages the model to be invariant to subgroup differences, and focus on class information shared by subgroups. We instantiate model patching with CAMEL, which (1) uses a CycleGAN to learn the intra-class, inter-subgroup augmentations, and (2) balances subgroup performance using a theoretically-motivated consistency regularizer. We demonstrate CAMEL's effectiveness on 3 benchmark datasets, with reductions in robust error up to 33% relative to the best baseline.
  arXiv  Detail & Related papers  (2020-08-15T20:01:23Z)
• Group Membership Verification with Privacy: Sparse or Dense? [21.365032455883178]
  Group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member. Recent contributions provide privacy for group membership protocols through the joint use of two mechanisms. This paper proposes a mathematical model for group membership verification allowing to reveal the impact of sparsity on both security, compactness, and verification performances.
  arXiv  Detail & Related papers  (2020-02-24T16:47:19Z)

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.