Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging

• URL: http://arxiv.org/abs/2308.04395v1
• Date: Tue, 8 Aug 2023 17:00:11 GMT
• Title: Data Augmentation-Based Unsupervised Domain Adaptation In Medical Imaging
• Authors: Sebastian N{\o}rgaard Llambias, Mads Nielsen, Mostafa Mehdipour Ghazi
• Abstract summary: We propose an unsupervised method for robust domain adaptation in brain MRI segmentation by leveraging MRI-specific augmentation techniques. The results show that our proposed approach achieves high accuracy, exhibits broad applicability, and showcases remarkable robustness against domain shift in various tasks.
• Score: 0.709016563801433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning-based models in medical imaging often struggle to generalize effectively to new scans due to data heterogeneity arising from differences in hardware, acquisition parameters, population, and artifacts. This limitation presents a significant challenge in adopting machine learning models for clinical practice. We propose an unsupervised method for robust domain adaptation in brain MRI segmentation by leveraging MRI-specific augmentation techniques. To evaluate the effectiveness of our method, we conduct extensive experiments across diverse datasets, modalities, and segmentation tasks, comparing against the state-of-the-art methods. The results show that our proposed approach achieves high accuracy, exhibits broad applicability, and showcases remarkable robustness against domain shift in various tasks, surpassing the state-of-the-art performance in the majority of cases.

Related papers

• LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models [59.961172635689664]
  "Knowledge Decomposition" aims to improve the performance on specific medical tasks. We propose a novel framework named Low-Rank Knowledge Decomposition (LoRKD) LoRKD explicitly separates gradients from different tasks by incorporating low-rank expert modules and efficient knowledge separation convolution.
  arXiv  Detail & Related papers  (2024-09-29T03:56:21Z)
• Cross-Modal Domain Adaptation in Brain Disease Diagnosis: Maximum Mean Discrepancy-based Convolutional Neural Networks [0.0]
  Brain disorders are a major challenge to global health, causing millions of deaths each year. Accurate diagnosis of these diseases relies heavily on advanced medical imaging techniques such as MRI and CT. The scarcity of annotated data poses a significant challenge in deploying machine learning models for medical diagnosis.
  arXiv  Detail & Related papers  (2024-05-06T07:44:46Z)
• Adaptive Affinity-Based Generalization For MRI Imaging Segmentation Across Resource-Limited Settings [1.5703963908242198]
  This paper introduces a novel relation-based knowledge framework by seamlessly combining adaptive affinity-based and kernel-based distillation. To validate our innovative approach, we conducted experiments on publicly available multi-source prostate MRI data.
  arXiv  Detail & Related papers  (2024-04-03T13:35:51Z)
• Boosting Few-Shot Learning with Disentangled Self-Supervised Learning and Meta-Learning for Medical Image Classification [8.975676404678374]
  We present a strategy for improving the performance and generalization capabilities of models trained in low-data regimes. The proposed method starts with a pre-training phase, where features learned in a self-supervised learning setting are disentangled to improve the robustness of the representations for downstream tasks. We then introduce a meta-fine-tuning step, leveraging related classes between meta-training and meta-testing phases but varying the level.
  arXiv  Detail & Related papers  (2024-03-26T09:36:20Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• Domain Generalization for Mammographic Image Analysis with Contrastive Learning [62.25104935889111]
  The training of an efficacious deep learning model requires large data with diverse styles and qualities. A novel contrastive learning is developed to equip the deep learning models with better style generalization capability. The proposed method has been evaluated extensively and rigorously with mammograms from various vendor style domains and several public datasets.
  arXiv  Detail & Related papers  (2023-04-20T11:40:21Z)
• Ambiguous Medical Image Segmentation using Diffusion Models [60.378180265885945]
  We introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent sampling process of diffusion. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks.
  arXiv  Detail & Related papers  (2023-04-10T17:58:22Z)
• 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)
• 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)
• Domain Shift in Computer Vision models for MRI data analysis: An Overview [64.69150970967524]
  Machine learning and computer vision methods are showing good performance in medical imagery analysis. Yet only a few applications are now in clinical use. Poor transferability of themodels to data from different sources or acquisition domains is one of the reasons for that.
  arXiv  Detail & Related papers  (2020-10-14T16:34:21Z)
• 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)

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.