Cross-Domain Distribution Alignment for Segmentation of Private Unannotated 3D Medical Images

• URL: http://arxiv.org/abs/2410.09210v1
• Date: Fri, 11 Oct 2024 19:28:10 GMT
• Title: Cross-Domain Distribution Alignment for Segmentation of Private Unannotated 3D Medical Images
• Authors: Ruitong Sun, Mohammad Rostami,
• Abstract summary: We introduce a new source-free Unsupervised Domain Adaptation (UDA) method to address this problem. Our idea is based on estimating the internally learned distribution of a relevant source domain by a base model. We demonstrate that our approach leads to SOTA performance on a real-world 3D medical dataset.
• Score: 20.206972068340843
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Manual annotation of 3D medical images for segmentation tasks is tedious and time-consuming. Moreover, data privacy limits the applicability of crowd sourcing to perform data annotation in medical domains. As a result, training deep neural networks for medical image segmentation can be challenging. We introduce a new source-free Unsupervised Domain Adaptation (UDA) method to address this problem. Our idea is based on estimating the internally learned distribution of a relevant source domain by a base model and then generating pseudo-labels that are used for enhancing the model refinement through self-training. We demonstrate that our approach leads to SOTA performance on a real-world 3D medical dataset.

Related papers

• Diffuse-UDA: Addressing Unsupervised Domain Adaptation in Medical Image Segmentation with Appearance and Structure Aligned Diffusion Models [31.006056670998852]
  The scarcity and complexity of voxel-level annotations in 3D medical imaging present significant challenges. This disparity affects the fairness of artificial intelligence algorithms in healthcare. We introduce Diffuse-UDA, a novel method leveraging diffusion models to tackle Unsupervised Domain Adaptation (UDA) in medical image segmentation.
  arXiv  Detail & Related papers  (2024-08-12T08:21:04Z)
• Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models [13.08275555017179]
  We propose ProMISe, a prompt-driven 3D medical image segmentation model using only a single point prompt. We evaluate our model on two public datasets for colon and pancreas tumor segmentations.
  arXiv  Detail & Related papers  (2023-10-30T16:49:03Z)
• Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning [57.43322536718131]
  We present a two-stage source-free domain adaptation (SFDA) framework for medical image segmentation. In the prototype-anchored feature alignment stage, we first utilize the weights of the pre-trained pixel-wise classifier as source prototypes. Then, we introduce the bi-directional transport to align the target features with class prototypes by minimizing its expected cost.
  arXiv  Detail & Related papers  (2023-07-19T06:07:12Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Anatomy-guided domain adaptation for 3D in-bed human pose estimation [62.3463429269385]
  3D human pose estimation is a key component of clinical monitoring systems. We present a novel domain adaptation method, adapting a model from a labeled source to a shifted unlabeled target domain. Our method consistently outperforms various state-of-the-art domain adaptation methods.
  arXiv  Detail & Related papers  (2022-11-22T11:34:51Z)
• Unsupervised Model Adaptation for Source-free Segmentation of Medical Images [15.820660013260584]
  unsupervised domain adaptation (UDA) can be used to combat the need for labeling images in a target domain. Most UDA approaches ensure target generalization by creating a shared source/target latent feature space. We propose an UDA algorithm for medical image segmentation that does not require access to source data during adaptation, and is thus capable in maintaining patient data privacy.
  arXiv  Detail & Related papers  (2022-11-02T01:01:19Z)
• PCA: Semi-supervised Segmentation with Patch Confidence Adversarial Training [52.895952593202054]
  We propose a new semi-supervised adversarial method called Patch Confidence Adrial Training (PCA) for medical image segmentation. PCA learns the pixel structure and context information in each patch to get enough gradient feedback, which aids the discriminator in convergent to an optimal state. Our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.
  arXiv  Detail & Related papers  (2022-07-24T07:45:47Z)
• Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation [49.59567529191423]
  We propose a novel semi-supervised learning framework for segmentation of volumetric images from new unlabeled domains. We jointly use supervised and contrastive learning, also introducing a contrastive pairing scheme that leverages similarity between nearby slices in 3D.
  arXiv  Detail & Related papers  (2022-03-07T19:02:26Z)
• Privacy Preserving Domain Adaptation for Semantic Segmentation of Medical Images [13.693640425403636]
  Unsupervised domain adaptation (UDA) is proposed to adapt a model to new modalities using solely unlabeled target domain data. We develop an algorithm for UDA in a privacy-constrained setting, where the source domain data is inaccessible. We demonstrate the effectiveness of our algorithm by comparing it to state-of-the-art medical image semantic segmentation approaches.
  arXiv  Detail & Related papers  (2021-01-02T22:12:42Z)
• Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization [59.5104563755095]
  We introduce a simple but effective approach to improve the generalization capability of deep neural networks in the field of medical imaging classification. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a representative feature space through variational encoding.
  arXiv  Detail & Related papers  (2020-09-27T12:30:30Z)
• Domain Adaptive Medical Image Segmentation via Adversarial Learning of Disease-Specific Spatial Patterns [6.298270929323396]
  We propose an unsupervised domain adaptation framework for boosting image segmentation performance across multiple domains. We enforce architectures to be adaptive to new data by rejecting improbable segmentation patterns and implicitly learning through semantic and boundary information. We demonstrate that recalibrating the deep networks on a few unlabeled images from the target domain improves the segmentation accuracy significantly.
  arXiv  Detail & Related papers  (2020-01-25T13:48:02Z)

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.