Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR Images

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

4 Scopus Citations
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Author(s)

  • Zhe Min
  • Fernando J. Bianco
  • Qianye Yang
  • Rachael Rodell
  • Dean Barratt
  • Yipeng Hu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis
Subtitle of host publication25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12–14, 2021, Proceedings
EditorsBartłomiej W. Papież, Mohammad Yaqub, Jianbo Jiao, Ana I. L. Namburete, J. Alison Noble
Place of PublicationCham
PublisherSpringer
Pages56-70
ISBN (Print)9783030804312
Publication statusPublished - 2021

Publication series

NameLecture Notes in Computer Science
Volume12722
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Title25th UK Conference on Medical Image Understanding and Analysis (MIUA 2021)
LocationUniversity of Oxford (Virtual)
PlaceUnited Kingdom
CityOxford
Period12 - 14 July 2021

Abstract

Prostate cancer (PCa) is one of the leading causes of death for men worldwide. Multi-parametric magnetic resonance (mpMR) imaging has emerged as a non-invasive diagnostic tool for detecting and localising prostate tumours by specialised radiologists. These radiological examinations, for example, for differentiating malignant lesions from benign prostatic hyperplasia in transition zones and for defining the boundaries of clinically significant cancer, remain challenging and highly skill-and-experience-dependent. We first investigate experimental results in developing object detection neural networks that are trained to predict the radiological assessment, using these high-variance labels. We further argue that such a computer-assisted diagnosis (CAD) system needs to have the ability to control the false-positive rate (FPR) or false-negative rate (FNR), in order to be usefully deployed in a clinical workflow, informing clinical decisions without further human intervention. However, training detection networks typically requires a multi-tasking loss, which is not trivial to be adapted for a direct control of FPR/FNR. This work in turn proposes a novel PCa detection network that incorporates a lesion-level cost-sensitive loss and an additional slice-level loss based on a lesion-to-slice mapping function, to manage the lesion- and slice-level costs, respectively. Our experiments based on 290 clinical patients concludes that 1) The lesion-level FNR was effectively reduced from 0.19 to 0.10 and the lesion-level FPR was reduced from 1.03 to 0.66 by changing the lesion-level cost; 2) The slice-level FNR was reduced from 0.19 to 0.00 by taking into account the slice-level cost; (3) Both lesion-level and slice-level FNRs were reduced with lower FP/FPR by changing the lesion-level or slice-level costs, compared with post-training threshold adjustment using networks without the proposed cost-aware training. For the PCa application of interest, the proposed CAD system is capable of substantially reducing FNR with a relatively preserved FPR, therefore is considered suitable for PCa screening applications.

Research Area(s)

  • False negative reduction, Multi-parametric resonance images, Object detection, Prostate cancer

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR Images. / Min, Zhe; Bianco, Fernando J.; Yang, Qianye et al.
Medical Image Understanding and Analysis: 25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12–14, 2021, Proceedings. ed. / Bartłomiej W. Papież; Mohammad Yaqub; Jianbo Jiao; Ana I. L. Namburete; J. Alison Noble. Cham: Springer, 2021. p. 56-70 (Lecture Notes in Computer Science; Vol. 12722).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review