Improving Pathological Structure Segmentation via Transfer Learning Across Diseases

Kaur, Barleen; Lemaître, Paul; Mehta, Raghav; Sepahvand, Nazanin Mohammadi; Precup, Doina; Arnold, Douglas L.

doi:10.1007/978-3-030-33391-1_11

Cited by 13 publications

(8 citation statements)

References 11 publications

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“…The second most popular strategy to apply transfer learning was fine-tuning certain parameters in a pretrained CNN [ 34 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 ]. The remaining approaches first optimized a feature extractor (typically a CNN or a SVM), and then trained a separated model (SVMs [ 30 , 45 , 147 , 148 , 149 ], long short-term memory networks [ 150 , 151 ], clustering methods [ 148 , 152 ], random forests [ 70 , 153 ], multilayer perceptrons [ 154 ], logistic regression [ 148 ], elastic net [ 155 ], CNNs [ 156 ]).…”

Section: Resultsmentioning

confidence: 99%

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

et al. 2021

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(1) Background: Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. Additionally, transfer learning is beneficial for reutilizing machine learning models that were trained to solve different (but related) tasks to the task of interest. The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging; (2) Methods: We performed a systematic literature search for articles that applied transfer learning to MR brain imaging tasks. We screened 433 studies for their relevance, and we categorized and extracted relevant information, including task type, application, availability of labels, and machine learning methods. Furthermore, we closely examined brain MRI-specific transfer learning approaches and other methods that tackled issues relevant to medical imaging, including privacy, unseen target domains, and unlabeled data; (3) Results: We found 129 articles that applied transfer learning to MR brain imaging tasks. The most frequent applications were dementia-related classification tasks and brain tumor segmentation. The majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). Only a few approaches utilized clearly brain MRI-specific methodology, and considered privacy issues, unseen target domains, or unlabeled data. We proposed a new categorization to group specific, widely-used approaches such as pretraining and fine-tuning CNNs; (4) Discussion: There is increasing interest in transfer learning for brain MRI. Well-known public datasets have clearly contributed to the popularity of Alzheimer’s diagnostics/prognostics and tumor segmentation as applications. Likewise, the availability of pretrained CNNs has promoted their utilization. Finally, the majority of the surveyed studies did not examine in detail the interpretation of their strategies after applying transfer learning, and did not compare their approach with other transfer learning approaches.

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Section: Resultsmentioning

confidence: 99%

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

et al. 2021

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“…This method is evaluated on the ISBI2015 dataset [89] for MRI segmentation. Kaur et al [90] propose to first pretrain a 3D U-Net on a source domain that has relevant diseases with a large number of samples, and then use a few labeled target data to fine-tune the network. Experiments on the BraTS dataset [52] show this strategy achieves a better performance than the network trained from scratch.…”

Section: A Supervised Deep Damentioning

confidence: 99%

Domain Adaptation for Medical Image Analysis: A Survey

Guan,

Liu

2021

Preprint

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Machine learning techniques used in computeraided medical image analysis usually suffer from the domain shift problem caused by different distributions between source/reference data and target data. As a promising solution, domain adaptation has attracted considerable attention in recent years. The aim of this paper is to survey the recent advances of domain adaptation methods in medical image analysis. We first present the motivation of introducing domain adaptation techniques to tackle domain heterogeneity issues for medical image analysis. Then we provide a review of recent domain adaptation models in various medical image analysis tasks. We categorize the existing methods into shallow and deep models, and each of them is further divided into supervised, semi-supervised and unsupervised methods. We also provide a brief summary of the benchmark medical image datasets that support current domain adaptation research. This survey will enable researchers to gain a better understanding of the current status, challenges and future directions of this energetic research field.

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“…Another UDA approach, based on domainadversarial neural networks (Ganin et al 2016), is used for multi-site brain lesion segmentation (Kamnitsas et al 2017). Several SDA approaches have been proposed for MRI classification or segmentation (Hosseini-Asl, Keynton, and El-Baz 2016;Valverde et al 2019;Kaur et al 2019). Unlearning the scanner bias (Dinsdale, Jenkinson, and Namburete 2020) is an SDA method for MRI harmonisation and manages to improve the performance in age prediction from MR images.…”

Section: Related Workmentioning

confidence: 99%

Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis

Wolleb¹,

Sandkühler²,

Bieder³

et al. 2021

Preprint

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Limited availability of large image datasets is a major issue in the development of accurate and generalizable machine learning methods in medicine. The limitations in the amount of data are mainly due to the use of different acquisition protocols, different hardware, and data privacy. At the same time, training a classification model on a small dataset leads to a poor generalization quality of the model. To overcome this issue, a combination of various image datasets of different provenance is often used, e.g., multi-site studies. However, if an additional dataset does not include all classes of the task, the learning of the classification model can be biased to the device or place of acquisition. This is especially the case for Magnetic Resonance (MR) images, where different MR scanners introduce a bias that limits the performance of the model. In this paper, we present a novel method that learns to ignore the scanner-related features present in the images, while learning features relevant for the classification task. We focus on a real-world scenario, where only a small dataset provides images of all classes. We exploit this circumstance by introducing specific additional constraints on the latent space, which lead the focus on disease-related rather than scannerspecific features. Our method Learn to Ignore outperforms state-of-the-art domain adaptation methods on a multi-site MRI dataset on a classification task between Multiple Sclerosis patients and healthy subjects.

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Improving Pathological Structure Segmentation via Transfer Learning Across Diseases

Cited by 13 publications

References 11 publications

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

Domain Adaptation for Medical Image Analysis: A Survey

Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis

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