Prostate Cancer Malignancy Detection and localization from mpMRI using auto-Deep Learning: One Step Closer to Clinical Utilization

Weiwei Zong; Eric Carver; Simeng Zhu; Eric Schaff; Daniel Chapman; Joon Lee; Hassan Bagher Ebadian; Indrin Chetty; Benjamin Movsas; Winston Wen; Tarik Alafif; Xiangyun Zong

arXiv · 2206.06235

Prostate Cancer Malignancy Detection and localization from mpMRI using auto-Deep Learning: One Step Closer to Clinical Utilization

Weiwei Zong·Eric Carver·Simeng Zhu·Eric Schaff·Daniel Chapman·Joon Lee·Hassan Bagher Ebadian·Indrin Chetty·Benjamin Movsas·Winston Wen·Tarik Alafif·Xiangyun Zong

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Abstract

Automatic 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 where we trained a customized convolutional neural network on a public cohort with 201 patients and the cropped 2D patches around the region of interest were used as the input, 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. Something different was peripheral zone (PZ) and central gland (CG) were trained and tested separately, the PZ detector and CG detector were demonstrated effectively in highlighting the most suspicious slices out of a sequence, hopefully to greatly ease the workload for the physicians.

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Weiwei Zong, Eric Carver, Simeng Zhu, Eric Schaff, Daniel Chapman, Joon Lee, Hassan Bagher Ebadian, Indrin Chetty, Benjamin Movsas, Winston Wen, Tarik Alafif, Xiangyun Zong. 2022-06-13. Prostate Cancer Malignancy Detection and localization from mpMRI using auto-Deep Learning: One Step Closer to Clinical Utilization. https://arxiv.org/abs/2206.06235

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