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

arXiv: https://arxiv.org/abs/2008.09352 · HTML: https://mlsanity.com/paper/2008.09352/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2020.

## Built on by (most cited first, 11 of 11)

- [WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma](https://mlsanity.com/paper/2204.06455/) (2022)
- [Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Images](https://mlsanity.com/paper/2202.07001/) (2022)
- [Benchmarking PathCLIP for Pathology Image Analysis](https://mlsanity.com/paper/2401.02651/) (2024)
- [Copycats: the many lives of a publicly available medical imaging dataset](https://mlsanity.com/paper/2402.06353/) (2024)
- [Weakly supervised pan-cancer segmentation tool](https://mlsanity.com/paper/2105.04269/) (2021)
- [VISTA-PATH: An interactive foundation model for pathology image segmentation and quantitative analysis in computational pathology](https://mlsanity.com/paper/2601.16451/) (2026)
- [Hoechst Is All You Need: Lymphocyte Classification with Deep Learning](https://mlsanity.com/paper/2107.04388/) (2021)
- [Efficient Self-Supervised Grading of Prostate Cancer Pathology](https://mlsanity.com/paper/2501.15520/) (2025)
- [A Deep Reinforcement Learning Framework for Rapid Diagnosis of Whole Slide Pathological Images](https://mlsanity.com/paper/2205.02850/) (2022)
- [S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation](https://mlsanity.com/paper/2411.12547/) (2024)
- [Cancer cytoplasm segmentation in hyperspectral cell image with data augmentation](https://mlsanity.com/paper/2507.03325/) (2025)

## Abstract (arXiv)

Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation. All methods were evaluated using the false positive rate, false negative rate, and DICE coefficient (DC). The DC ranged from 0.7354±0.1149 to 0.8372±0.0858. The DC of the best method was close to the inter-observer agreement (0.8398±0.0890). All methods were based on deep learning and categorized into two groups: multi-model method and single model method. In general, multi-model methods were significantly better (textitp<0.01) than single model methods, with mean DC of 0.7966 and 0.7544, respectively. Deep learning based methods could potentially help pathologists find suspicious regions for further analysis of lung cancer in WSI.