Domain shift in computer vision models for MRI data analysis: an overview

Kondrateva, Ekaterina; Pominova, Marina; Popova, Elena; Sharaev, Maxim; Bernstein, Alexander; Burnaev, Evgeny

doi:10.1117/12.2587872

Cited by 24 publications

(11 citation statements)

References 42 publications

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“…Magnetic Resonance Imaging (MRI) data is even more susceptible to changes in the acquisition conditions than CTs, as there is no consensus on the calibration of intensity values. This causes the performance of segmentation models trained on MR tasks to deteriorate on OOD data ( Zakazov et al, 2021 , Kondrateva et al, 2021 ).…”

Section: Resultsmentioning

confidence: 99%

Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation

González

Gotkowski

Fuchs

et al. 2022

Medical Image Analysis

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Section: Resultsmentioning

confidence: 99%

Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation

González

Gotkowski

Fuchs

et al. 2022

Medical Image Analysis

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“…Such methods used domain-invariant features, ignoring the domain shift in data distributions. Kondrateva et al (2021) analyzed and compared different MR imaging studies that tackle the domain-shift problem. They focused on advanced data processing, auto-encoding neural networks and their domain-invariant variations, model architecture enhancing, and feature training, as well as a prediction in a domain-invariant latent space approaches.…”

Section: Classificationmentioning

confidence: 99%

Deep Learning in Large and Multi-Site Structural Brain MR Imaging Datasets

Bento

Fantini

Park

et al. 2022

Front. Neuroinform.

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Large, multi-site, heterogeneous brain imaging datasets are increasingly required for the training, validation, and testing of advanced deep learning (DL)-based automated tools, including structural magnetic resonance (MR) image-based diagnostic and treatment monitoring approaches. When assembling a number of smaller datasets to form a larger dataset, understanding the underlying variability between different acquisition and processing protocols across the aggregated dataset (termed “batch effects”) is critical. The presence of variation in the training dataset is important as it more closely reflects the true underlying data distribution and, thus, may enhance the overall generalizability of the tool. However, the impact of batch effects must be carefully evaluated in order to avoid undesirable effects that, for example, may reduce performance measures. Batch effects can result from many sources, including differences in acquisition equipment, imaging technique and parameters, as well as applied processing methodologies. Their impact, both beneficial and adversarial, must be considered when developing tools to ensure that their outputs are related to the proposed clinical or research question (i.e., actual disease-related or pathological changes) and are not simply due to the peculiarities of underlying batch effects in the aggregated dataset. We reviewed applications of DL in structural brain MR imaging that aggregated images from neuroimaging datasets, typically acquired at multiple sites. We examined datasets containing both healthy control participants and patients that were acquired using varying acquisition protocols. First, we discussed issues around Data Access and enumerated the key characteristics of some commonly used publicly available brain datasets. Then we reviewed methods for correcting batch effects by exploring the two main classes of approaches: Data Harmonization that uses data standardization, quality control protocols or other similar algorithms and procedures to explicitly understand and minimize unwanted batch effects; and Domain Adaptation that develops DL tools that implicitly handle the batch effects by using approaches to achieve reliable and robust results. In this narrative review, we highlighted the advantages and disadvantages of both classes of DL approaches, and described key challenges to be addressed in future studies.

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“…Consequently, the application of DL methods in clinically realistic environments results in poor generalization and performance, despite the expert-level performance achieved during development (Yasaka and Abe, 2018). A major reason for this is the existence of a domain shift (Kondrateva et al, 2021) in data acquired across different hospitals and scanners, such as the short-axis CMR images used in this study (Fig. 1).…”

Section: Introductionmentioning

confidence: 95%