One class to rule them all: detection and classification of prostate tumors presence in bi-parametric MRI based on auto-encoders

Alvaro Fernandez-Quilez; Habib Ullah; Trygve Eftestøl; Svein Reidar Kjosavik; Ketil Oppedal

doi:10.1117/12.2602884

4 April 2022 One class to rule them all: detection and classification of prostate tumors presence in bi-parametric MRI based on auto-encoders

Alvaro Fernandez-Quilez, Habib Ullah, Trygve Eftestøl, Svein Reidar Kjosavik, Ketil Oppedal

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120331N (2022) https://doi.org/10.1117/12.2602884
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Prostate Cancer (PCa) is the fifth leading cause of death and the second most common cancer diagnosed among men worldwide. Current diagnostic practices suffer from a substantial overdiagnosis of indolent tumors. Deep Learning (DL) holds promise in automatizing prostate MRI analysis and enabling computer-assisted systems able to improve current practices. Nevertheless, large amounts of annotated data are commonly required for DL systems success. On the other hand, an experienced clinician is typically able to discern between a normal (no lesion) and an abnormal (contains PCa lesions) case after seeing a few normal cases, ultimately reducing the amount of data required to detect abnormal cases. This work exploits such an ability by making use of normal cases at training time and learning their distribution through auto-encoder-based architectures. We propose to use a threshold approach based on interquartile range to discriminate between normal and abnormal cases at evaluation time, quantified through the area under the curve (AUC). Furthermore, we show the ability of our method to detect lesions in those cases deemed as abnormal in an unsupervised way in T2w and apparent diffusion coefficient maps (ADC) MRI modalities.

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Alvaro Fernandez-Quilez, Habib Ullah, Trygve Eftestøl, Svein Reidar Kjosavik, and Ketil Oppedal "One class to rule them all: detection and classification of prostate tumors presence in bi-parametric MRI based on auto-encoders", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120331N (4 April 2022); https://doi.org/10.1117/12.2602884

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