A Deep Learning System to Predict the Histopathological Results From Urine Cytopathological Images.

ABSTRACT:

Background

Although deep learning systems (DLSs) have been developed to diagnose urine cytology, more evidence is required to prove if such systems can predict histopathology results as well.

Methods

We retrospectively retrieved urine cytology slides and matched histological results. High-power field panel images were annotated by a certified urological pathologist. A deep learning system was designed with a ResNet101 Faster R-CNN (faster region-based convolutional neural network). It was firstly built to spot cancer cells. Then, it was directly used to predict the likelihood of the presence of tissue malignancy.

Results

We retrieved 441 positive cases and 395 negative cases. The development involved 387 positive cases, accounting for 2,668 labeled cells, to train the

SUBMITTER: Liu Y 

PROVIDER: S-EPMC9170952 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature