Related papers: Cross-Domain Segmentation with Adversarial Loss and Covariate Shift for Biomedical Imaging

Cross-Domain Segmentation with Adversarial Loss and Covariate Shift for Biomedical Imaging

URL: http://arxiv.org/abs/2006.04390v1
Date: Mon, 8 Jun 2020 07:35:55 GMT
Title: Cross-Domain Segmentation with Adversarial Loss and Covariate Shift for Biomedical Imaging
Authors: Bora Baydar, Savas Ozkan, A. Emre Kavur, N. Sinem Gezer, M. Alper Selver, Gozde Bozdagi Akar
Abstract summary: This manuscript aims to implement a novel model that can learn robust representations from cross-domain data by encapsulating distinct and shared patterns from different modalities. The tests on CT and MRI liver data acquired in routine clinical trials show that the proposed model outperforms all other baseline with a large margin.
Score: 2.1204495827342438
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Despite the widespread use of deep learning methods for semantic segmentation of images that are acquired from a single source, clinicians often use multi-domain data for a detailed analysis. For instance, CT and MRI have advantages over each other in terms of imaging quality, artifacts, and output characteristics that lead to differential diagnosis. The capacity of current segmentation techniques is only allow to work for an individual domain due to their differences. However, the models that are capable of working on all modalities are essentially needed for a complete solution. Furthermore, robustness is drastically affected by the number of samples in the training step, especially for deep learning models. Hence, there is a necessity that all available data regardless of data domain should be used for reliable methods. For this purpose, this manuscript aims to implement a novel model that can learn robust representations from cross-domain data by encapsulating distinct and shared patterns from different modalities. Precisely, covariate shift property is retained with structural modification and adversarial loss where sparse and rich representations are obtained. Hence, a single parameter set is used to perform cross-domain segmentation task. The superiority of the proposed method is that no information related to modalities are provided in either training or inference phase. The tests on CT and MRI liver data acquired in routine clinical workflows show that the proposed model outperforms all other baseline with a large margin. Experiments are also conducted on Covid-19 dataset that it consists of CT data where significant intra-class visual differences are observed. Similarly, the proposed method achieves the best performance.

Related papers

Weakly supervised deep learning model with size constraint for prostate cancer detection in multiparametric MRI and generalization to unseen domains [0.90668179713299]
We show that the model achieves on-par performance with strong fully supervised baseline models. We also observe a performance decrease for both fully supervised and weakly supervised models when tested on unseen data domains.
arXiv Detail & Related papers (2024-11-04T12:24:33Z)
DG-TTA: Out-of-domain medical image segmentation through Domain Generalization and Test-Time Adaptation [43.842694540544194]
We propose to combine domain generalization and test-time adaptation to create a highly effective approach for reusing pre-trained models in unseen target domains. We demonstrate that our method, combined with pre-trained whole-body CT models, can effectively segment MR images with high accuracy.
arXiv Detail & Related papers (2023-12-11T10:26:21Z)
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning [53.00683059396803]
Mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images. We propose a decision-based MIM that utilizes reinforcement learning (RL) to automatically search for optimal image masking ratio and masking strategy. Our approach has a significant advantage over alternative self-supervised methods on the task of neuron segmentation.
arXiv Detail & Related papers (2023-10-06T10:40:46Z)
Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis [55.03872260158717]
Resting-state MRI functional (rs-fMRI) is increasingly employed in multi-site research to aid neurological disorder analysis. Many methods have been proposed to reduce fMRI heterogeneity between source and target domains. But acquiring source data is challenging due to concerns and/or data storage burdens in multi-site studies. We design a source-free collaborative domain adaptation framework for fMRI analysis, where only a pretrained source model and unlabeled target data are accessible.
arXiv Detail & Related papers (2023-08-24T01:30:18Z)
Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation [27.49427483473792]
In real-world scenarios, it is common for models to encounter data from new and different domains to which they were not exposed to during training. domain generalization (DG) is a promising direction as it enables models to handle data from previously unseen domains. We introduce a novel DG method called Adversarial Intensity Attack (AdverIN), which leverages adversarial training to generate training data with an infinite number of styles.
arXiv Detail & Related papers (2023-04-05T19:40:51Z)
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
arXiv Detail & Related papers (2023-02-03T13:50:25Z)
FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain [0.8376091455761259]
A novel deep learning method is proposed for fast and accurate segmentation of the human brain into 132 regions. The proposed model uses an efficient U-Net-like network and benefits from the intersection points of different views and hierarchical relations. The proposed method can be applied to brain MRI data including skull or any other artifacts without preprocessing the images or a drop in performance.
arXiv Detail & Related papers (2022-08-30T16:06:07Z)
Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme. Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor. The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
arXiv Detail & Related papers (2021-05-17T10:11:45Z)
Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains [68.73614619875814]
We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation. Experimental results show that our approach outperforms many state-of-the-art generalization methods consistently across all six settings of unseen domains.
arXiv Detail & Related papers (2020-07-04T07:56:02Z)
MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data [75.73881040581767]
We propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations. Our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.
arXiv Detail & Related papers (2020-02-09T14:11:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.