Author:
Zong Weiwei,Carver Eric,Zhu Simeng,Schaff Eric,Chapman Daniel,Lee Joon,Bagher-Ebadian Hassan,Movsas Benjamin,Wen Winston,Alafif Tarik,Zong Xiangyun
Abstract
AbstractAutomatic diagnosis of malignant prostate cancer patients from mpMRI has been studied heavily in the past years. Model interpretation and domain drift have been the main road blocks for clinical utilization. As an extension from our previous work we trained on a public cohort with 201 patients and the cropped 2.5D slices of the prostate glands were used as the input, and the optimal model were searched in the model space using autoKeras. As an innovative move, peripheral zone (PZ) and central gland (CG) were trained and tested separately, the PZ detector and CG detector were demonstrated effective in highlighting the most suspicious slices out of a sequence, hopefully to greatly ease the workload for the physicians.
Publisher
Springer Science and Business Media LLC
Cited by
3 articles.
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