Domain Adaptation for Medical Image Analysis: A Survey

Guan, Hao; Liu, Mingxia

doi:10.48550/arxiv.2102.09508

Cited by 8 publications

(7 citation statements)

References 125 publications

(206 reference statements)

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“…input of Network-1). Similarly, an approach based on cross-modality domain adaptation 51 could be investigated to directly adapt the CT-specific algorithm of Hofmanninger et al 46 to segment MRI data. Besides the difficulty of implementing such techniques, they might require several CT and MR data that often are unavailable.…”

Section: Lumentioning

confidence: 99%

MRI lung lobe segmentation in pediatric cystic fibrosis patients using a recurrent neural network trained with publicly accessible CT datasets

Pusterla

Heule

Santini

et al. 2022

Magnetic Resonance in Med

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Funding information Swiss Cystic Fibrosis Society (CFCH)Purpose: To introduce a widely applicable workflow for pulmonary lobe segmentation of MR images using a recurrent neural network (RNN) trained with chest CT datasets. The feasibility is demonstrated for 2D coronal ultrafast balanced SSFP (ufSSFP) MRI.Methods: Lung lobes of 250 publicly accessible CT datasets of adults were segmented with an open-source CT-specific algorithm. To match 2D ufSSFP MRI data of pediatric patients, both CT data and segmentations were translated into pseudo-MR images that were masked to suppress anatomy outside the lung. Network-1 was trained with pseudo-MR images and lobe segmentations and then applied to 1000 masked ufSSFP images to predict lobe segmentations. These outputs were directly used as targets to train Network-2 and Network-3 with non-masked ufSSFP data as inputs, as well as an additional whole-lung mask as input for Network-2. Network predictions were compared to reference manual lobe segmentations of ufSSFP data in 20 pediatric cystic fibrosis patients. Manual lobe segmentations were performed by splitting available whole-lung segmentations into lobes. Results:Network-1 was able to segment the lobes of ufSSFP images, and Network-2 and Network-3 further increased segmentation accuracy and robustness. The average all-lobe Dice similarity coefficients were 95.0 ± 2.8 (mean ± pooled SD [%]) and 96.4 ± 2.5, 93.0 ± 2.0; and the average median Hausdorff distances were 6.1 ± 0.9 (mean ± SD [mm]), 5.3 ± 1.1, 7.1 ± 1.3 for Network-1, Network-2, and Network-3, respectively. Conclusion:Recurrent neural network lung lobe segmentation of 2D ufSSFP imaging is feasible, in good agreement with manual segmentations. The proposed workflow might provide access to automated lobe segmentations for various lung MRI examinations and quantitative analyses.

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Section: Lumentioning

confidence: 99%

MRI lung lobe segmentation in pediatric cystic fibrosis patients using a recurrent neural network trained with publicly accessible CT datasets

Pusterla

Heule

Santini

et al. 2022

Magnetic Resonance in Med

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show abstract

“…In this context, domain adaptation and generalisation have become relevant research areas. Several methods attempt to learn domain invariant representations by anticipating the distribution difference between training and test [11,12,13,14,15]. However, these approaches usually require prior knowledge about the test data, such as a small subset of (possibly labelled) images from the test distribution.…”

Section: Tackling Distribution Shiftsmentioning

confidence: 99%

Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts

Valvano¹,

Leo²,

Tsaftaris³

2021

Preprint

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Thanks to their ability to learn flexible data-driven losses, Generative Adversarial Networks (GANs) are an integral part of many semi-and weakly-supervised methods for medical image segmentation. GANs jointly optimise a generator and an adversarial discriminator on a set of training data. After training has completed, the discriminator is usually discarded and only the generator is used for inference. But should we discard discriminators? In this work, we argue that training stable discriminators produces expressive loss functions that we can re-use at inference to detect and correct segmentation mistakes. First, we identify key challenges and suggest possible solutions to make discriminators re-usable at inference. Then, we show that we can combine discriminators with image reconstruction costs (via decoders) to further improve the model. Our method is simple and improves the test-time performance of pre-trained GANs. Moreover, we show that it is compatible with standard post-processing techniques and it has potentials to be used for Online Continual Learning. With our work, we open new research avenues for re-using adversarial discriminators at inference.

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“…Domain adaptation methods have been successfully used to minimise the distance between the underlying distributions of the training and test datasets, i.e. a model trained on a given dataset (source distribution) is enabled to perform well on a different dataset (target distribution) via domain adaptation [3,10,14,15]. However, such methods require knowledge of the target distribution, which is not always readily available.…”

Section: Domain Generalisationmentioning

confidence: 99%

Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification

Bevan¹,

Atapour-Abarghouei²

2021

Preprint

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Convolutional Neural Networks have demonstrated dermatologist-level performance in the classification of melanoma and other skin lesions, but prediction irregularities due to biases seen within the training data are an issue that should be addressed before widespread deployment is possible. In this work, we robustly remove bias and spurious variation from an automated melanoma classification pipeline using two leading bias 'unlearning' techniques. We show that the biases introduced by surgical markings and rulers presented in previous studies can be reasonably mitigated using these bias removal methods. We also demonstrate the generalisation benefits of 'unlearning' spurious variation relating to the imaging instrument used to capture lesion images. Contributions of this work include the application of different debiasing techniques for artefact bias removal and the concept of instrument bias 'unlearning' for domain generalisation in melanoma detection. Our experimental results provide evidence that the effects of each of the aforementioned biases are notably reduced, with different debiasing techniques excelling at different tasks.

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Domain Adaptation for Medical Image Analysis: A Survey

Cited by 8 publications

References 125 publications

MRI lung lobe segmentation in pediatric cystic fibrosis patients using a recurrent neural network trained with publicly accessible CT datasets

MRI lung lobe segmentation in pediatric cystic fibrosis patients using a recurrent neural network trained with publicly accessible CT datasets

Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts

Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification

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