Reinforcement learning for active modality selection during diagnosis

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• dc.contributor.author Bernardino Perez, Gabriel
• dc.contributor.author Jonsson, Anders, 1973-
• dc.contributor.author Loncaric, Filip
• dc.contributor.author Martí Castellote, Pablo Miki
• dc.contributor.author Sitges, Marta
• dc.contributor.author Clarysse, Patrick
• dc.contributor.author Duchateau, Nicolas
• dc.date.accessioned 2023-03-07T08:09:28Z
• dc.date.issued 2022
• dc.description Comunicació presentada a 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022), celebrat del 18 al 22 de setembre de 2022 a Sentosa, Singapur.
• dc.description.abstract Diagnosis through imaging generally requires the combination of several modalities. Algorithms for data fusion allow merging information from different sources, mostly combining all images in a single step. In contrast, much less attention has been given to the incremental addition of new data descriptors, and the consideration of their costs (which can cover economic costs but also patient comfort and safety). In this work, we formalise clinical diagnosis of a patient as a sequential process of decisions, each of these decisions being whether to take an additional acquisition, or, if there is enough information, to end the examination and produce a diagnosis. We formulate the goodness of a diagnosis process as a combination of the classification accuracy minus the cost of the acquired modalities. To obtain a policy, we apply reinforcement learning, which recommends the next modality to incorporate based on data acquired at previous stages and aiming at maximising the accuracy/cost trade-off. This policy therefore performs medical diagnosis and patient-wise feature selection simultaneously. We demonstrate the relevance of this strategy on two binary classification datasets: a subset of a public heart disease database, including 531 instances with 11 scalar features, and a private echocardiographic dataset including signals from 5 standard image sequences used to assess cardiac function (2 speckle tracking, 2 flow Doppler and tissue Doppler), from 188 patients suffering hypertension, and 60 controls. For each individual, our algorithm allows acquiring only the modalities relevant for the diagnosis, avoiding low-information acquisitions, which both resulted in higher stability of the chosen modalities and better classification performance under a limited budget.
• dc.description.sponsorship The authors acknowledge the partial support from the French ANR (LABEX PRIMES of Univ. Lyon [ANR-11-LABX-0063] and the JCJC project “MIC-MAC” [ANR-19-CE45-0005]) and the Spanish AEI [PID2019-108141GB-I00].
• dc.format.mimetype application/pdf
• dc.identifier.citation Bernardino G, Jonsson A, Loncaric F, Martí Castellote PM, Sitges M, Clarysse P, Duchateau N. Reinforcement learning for active modality selection during diagnosis. In: Wang L, Dou Q, Fletcher PT, Speidel S, Li S, editors. Medical Image Computing and Computer Assisted Intervention (MICCAI 2022): 25th International Conference; 2022 Sep 18-22; Sentosa Island, Singapore. Cham: Springer; 2022. p. 592-601. DOI: 10.1007/978-3-031-16431-6_56
• dc.identifier.doi http://dx.doi.org/10.1007/978-3-031-16431-6_56
• dc.identifier.issn 0302-9743
• dc.identifier.uri http://hdl.handle.net/10230/56079
• dc.language.iso eng
• dc.publisher Springer
• dc.relation.ispartof Wang L, Dou Q, Fletcher PT, Speidel S, Li S, editors. Medical Image Computing and Computer Assisted Intervention (MICCAI 2022): 25th International Conference; 2022 Sep 18-22; Sentosa Island, Singapore. Cham: Springer; 2022. p. 592-601.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-108141GB-I00
• dc.rights © Springer This is a author's accepted manuscript of: Bernardino G, Jonsson A, Loncaric F, Martí Castellote PM, Sitges M, Clarysse P, Duchateau N. Reinforcement learning for active modality selection during diagnosis. In: Wang L, Dou Q, Fletcher PT, Speidel S, Li S, editors. Medical Image Computing and Computer Assisted Intervention (MICCAI 2022): 25th International Conference; 2022 Sep 18-22; Sentosa Island, Singapore. Cham: Springer; 2022. p. 592-601. The final version is available online at: http://dx.doi.org/10.1007/978-3-031-16431-6_56
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Computer aided diagnosis
• dc.subject.keyword Reinforcement learning
• dc.subject.keyword Active feature selection
• dc.subject.keyword Acquisition costs
• dc.subject.keyword Cardiac imaging
• dc.title Reinforcement learning for active modality selection during diagnosis
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion