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Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation

Buddenkotte, Thomas (författare)
Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom; Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Department for Diagnostic and Interventional Radiology and Nuclear Medicine, University Hospital Hamburg-Eppendorf, Hamburg, Germany; Jung diagnostics GmbH, Hamburg, Germany
Escudero Sanchez, Lorena (författare)
Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
Crispin-Ortuzar, Mireia (författare)
Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom; Department of Oncology, University of Cambridge, Cambridge, United Kingdom
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Woitek, Ramona (författare)
Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom; Medical Image Analysis & Artificial Intelligence (MIAAI), Department of Medicine, Danube Private University, Krems, Austria
McCague, Cathal (författare)
Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom
Brenton, James D. (författare)
Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom; Department of Oncology, University of Cambridge, Cambridge, United Kingdom
Öktem, Ozan, 1969- (författare)
KTH,Numerisk analys, NA
Sala, Evis (författare)
Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom; Dipartimento di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy; Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
Rundo, Leonardo (författare)
Department of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom; Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Italy
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 (creator_code:org_t)
Elsevier Ltd, 2023
2023
Engelska.
Ingår i: Computers in Biology and Medicine. - : Elsevier Ltd. - 0010-4825 .- 1879-0534. ; 163
Relaterad länk:
https://doi.org/10.1...
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https://urn.kb.se/re...
https://doi.org/10.1...
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  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • Uncertainty quantification in automated image analysis is highly desired in many applications. Typically, machine learning models in classification or segmentation are only developed to provide binary answers; however, quantifying the uncertainty of the models can play a critical role for example in active learning or machine human interaction. Uncertainty quantification is especially difficult when using deep learning-based models, which are the state-of-the-art in many imaging applications. The current uncertainty quantification approaches do not scale well in high-dimensional real-world problems. Scalable solutions often rely on classical techniques, such as dropout, during inference or training ensembles of identical models with different random seeds to obtain a posterior distribution. In this paper, we present the following contributions. First, we show that the classical approaches fail to approximate the classification probability. Second, we propose a scalable and intuitive framework for uncertainty quantification in medical image segmentation that yields measurements that approximate the classification probability. Third, we suggest the usage of k-fold cross-validation to overcome the need for held out calibration data. Lastly, we motivate the adoption of our method in active learning, creating pseudo-labels to learn from unlabeled images and human–machine collaboration.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)

Nyckelord

Deep learning
Segmentation
Uncertainty quantification

Publikations- och innehållstyp

ref (ämneskategori)
art (ämneskategori)

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