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  • Buddenkotte, ThomasDepartment 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 (author)

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

  • Article/chapterEnglish2023

Publisher, publication year, extent ...

  • Elsevier Ltd,2023
  • printrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:kth-331437
  • https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-331437URI
  • https://doi.org/10.1016/j.compbiomed.2023.107096DOI

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  • Language:English
  • Summary in:English

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  • Subject category:ref swepub-contenttype
  • Subject category:art swepub-publicationtype

Notes

  • QC 20230710
  • 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.

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Added entries (persons, corporate bodies, meetings, titles ...)

  • Escudero Sanchez, LorenaDepartment of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom (author)
  • Crispin-Ortuzar, MireiaCancer 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 (author)
  • Woitek, RamonaDepartment 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 (author)
  • McCague, CathalDepartment of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom (author)
  • Brenton, James D.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 (author)
  • Öktem, Ozan,1969-KTH,Numerisk analys, NA(Swepub:kth)u1q7d7sf (author)
  • Sala, EvisDepartment 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 (author)
  • Rundo, LeonardoDepartment 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 (author)
  • 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, GermanyDepartment of Radiology, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, United Kingdom (creator_code:org_t)

Related titles

  • In:Computers in Biology and Medicine: Elsevier Ltd1630010-48251879-0534

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