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

no code implementations • 21 Aug 2020 • Zhang Li, Jiehua Zhang, Tao Tan, Xichao Teng, Xiaoliang Sun, Yang Li, Lihong Liu, Yang Xiao, Byungjae Lee, Yilong Li, Qianni Zhang, Shujiao Sun, Yushan Zheng, Junyu Yan, Ni Li, Yiyu Hong, Junsu Ko, Hyun Jung, Yanling Liu, Yu-cheng Chen, Ching-Wei Wang, Vladimir Yurovskiy, Pavel Maevskikh, Vahid Khanagha, Yi Jiang, Xiangjun Feng, Zhihong Liu, Daiqiang Li, Peter J. Schüffler, Qifeng Yu, Hui Chen, Yuling Tang, Geert Litjens

All methods were based on deep learning and categorized into two groups: multi-model method and single model method.

Segmentation

A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancer

no code implementations • 30 May 2018 • Byungjae Lee, Kyunghyun Paeng

Recent advances in convolutional neural networks (CNN) have shown significant improvements in histological slide analysis, but accuracy is not optimized because of the difficulty in the handling of gigapixel images.

General Classification

Multi-Class Multi-Object Tracking using Changing Point Detection

no code implementations • 30 Aug 2016 • Byungjae Lee, Enkhbayar Erdenee, Songguo Jin, Phill Kyu Rhee

The ensemble of convolutional neural network (CNN) based object detector and Lucas-Kanede Tracker (KLT) based motion detector is employed to compute the likelihoods of foreground regions as the detection responses of different object classes.

Multi-Object Tracking Multiple Object Tracking +1