EvidenceCap: Towards trustworthy medical image segmentation via evidential identity cap

Yuan, Xuedong; Shen, Xiaojing; Chen, Yidi; Wang, Meng; Goh, Rick Siow Mong; Liu, Yong; Fu, Huazhu

doi:10.21203/rs.3.rs-2558155/v1

Cited by 6 publications

(2 citation statements)

References 36 publications

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“…Various strategies have been proposed for the prediction of reconstruction uncertainty with methods varying in prevalence, scalability, and practical applicability. 36 In a Bayesian treatment, the posterior distribution over the network parameters given the training data is modeled. 22 In contrast, deep ensembling allows for a more convenient and simplistic implementation of the epistemic uncertainty estimation.…”

Section: Theorymentioning

confidence: 99%

Predictive uncertainty in deep learning–based MR image reconstruction using deep ensembles: Evaluation on the fastMRI data set

Küstner,

Hammernik,

Rueckert

et al. 2024

Magnetic Resonance in Med

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PurposeTo estimate pixel‐wise predictive uncertainty for deep learning–based MR image reconstruction and to examine the impact of domain shifts and architecture robustness.MethodsUncertainty prediction could provide a measure for robustness of deep learning (DL)–based MR image reconstruction from undersampled data. DL methods bear the risk of inducing reconstruction errors like in‐painting of unrealistic structures or missing pathologies. These errors may be obscured by visual realism of DL reconstruction and thus remain undiscovered. Furthermore, most methods are task‐agnostic and not well calibrated to domain shifts. We propose a strategy that estimates aleatoric (data) and epistemic (model) uncertainty, which entails training a deep ensemble (epistemic) with nonnegative log‐likelihood (aleatoric) loss in addition to the conventional applied losses terms. The proposed procedure can be paired with any DL reconstruction, enabling investigations of their predictive uncertainties on a pixel level. Five different architectures were investigated on the fastMRI database. The impact on the examined uncertainty of in‐distributional and out‐of‐distributional data with changes to undersampling pattern, imaging contrast, imaging orientation, anatomy, and pathology were explored.ResultsPredictive uncertainty could be captured and showed good correlation to normalized mean squared error. Uncertainty was primarily focused along the aliased anatomies and on hyperintense and hypointense regions. The proposed uncertainty measure was able to detect disease prevalence shifts. Distinct predictive uncertainty patterns were observed for changing network architectures.ConclusionThe proposed approach enables aleatoric and epistemic uncertainty prediction for DL‐based MR reconstruction with an interpretable examination on a pixel level.

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Section: Theorymentioning

confidence: 99%

Predictive uncertainty in deep learning–based MR image reconstruction using deep ensembles: Evaluation on the fastMRI data set

Küstner,

Hammernik,

Rueckert

et al. 2024

Magnetic Resonance in Med

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“…While previous systematic and scoping reviews have covered the topics of UQ in healthcare generally [25,31] and in relation to medical imaging [32][33][34], these studies lacked any explicit focus on RT-related applications. Therefore, we conducted this scoping review to synthesize current trends for UQ in RT and provide an outlook for the future of this important research area for clinicians and researchers.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Uncertainty Quantification in Radiotherapy Applications - A Scoping Review

Wahid,

Kaffey,

Farris

et al. 2024

Preprint

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Background/purpose: The use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the need for effective uncertainty quantification (UQ) methods. The purpose of this study was to scope existing literature related to UQ in RT, identify areas of improvement, and determine future directions. Methods: We followed the PRISMA-ScR scoping review reporting guidelines. We utilized the population (human cancer patients), concept (utilization of AI UQ), context (radiotherapy applications) framework to structure our search and screening process. We conducted a systematic search spanning seven databases, supplemented by manual curation, up to January 2024. Our search yielded a total of 8980 articles for initial review. Manuscript screening and data extraction was performed in Covidence. Data extraction categories included general study characteristics, RT characteristics, AI characteristics, and UQ characteristics. Results: We identified 56 articles published from 2015-2024. 10 domains of RT applications were represented; most studies evaluated auto-contouring (50%), followed by image-synthesis (13%), and multiple applications simultaneously (11%). 12 disease sites were represented, with head and neck cancer being the most common disease site independent of application space (32%). Imaging data was used in 91% of studies, while only 13% incorporated RT dose information. Most studies focused on failure detection as the main application of UQ (60%), with Monte Carlo dropout being the most commonly implemented UQ method (32%) followed by ensembling (16%). 55% of studies did not share code or datasets. Conclusion: Our review revealed a lack of diversity in UQ for RT applications beyond auto-contouring. Moreover, there was a clear need to study additional UQ methods, such as conformal prediction. Our results may incentivize the development of guidelines for reporting and implementation of UQ in RT.

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Uncertainty-Informed Mutual Learning for Joint Medical Image Classification and Segmentation

Ren,

Zou,

Liu

et al. 2023

Lecture Notes in Computer Science

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EvidenceCap: Towards trustworthy medical image segmentation via evidential identity cap

Cited by 6 publications

References 36 publications

Predictive uncertainty in deep learning–based MR image reconstruction using deep ensembles: Evaluation on the fastMRI data set

Predictive uncertainty in deep learning–based MR image reconstruction using deep ensembles: Evaluation on the fastMRI data set

Artificial Intelligence Uncertainty Quantification in Radiotherapy Applications - A Scoping Review

Uncertainty-Informed Mutual Learning for Joint Medical Image Classification and Segmentation

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