English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Accurate segmentation of lung cancer in pathology slides is a critical stepin improving patient care. We proposed the ACDC@LungHP (Automatic CancerDetection and Classification in Whole-slide Lung Histopathology) challenge forevaluating different computer-aided diagnosis (CADs) methods on the automaticdiagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation(pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using anannotated dataset of 150 training images and 50 test images from 200 patients.This paper reviews this challenge and summarizes the top 10 submitted methodsfor lung cancer segmentation. All methods were evaluated using the falsepositive rate, false negative rate, and DICE coefficient (DC). The DC rangedfrom 0.7354$\pm$0.1149 to 0.8372$\pm$0.0858. The DC of the best method wasclose to the inter-observer agreement (0.8398$\pm$0.0890). All methods werebased on deep learning and categorized into two groups: multi-model method andsingle model method. In general, multi-model methods were significantly better($\textit{p}$<$0.01$) than single model methods, with mean DC of 0.7966 and0.7544, respectively. Deep learning based methods could potentially helppathologists find suspicious regions for further analysis of lung cancer inWSI.

Deep Learning Methods for Lung Cancer Segmentation in Whole-slide Histopathology Images -- the ACDC@LungHP Challenge 2019