WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma

no code implementations • 13 Apr 2022 • Chu Han, Xipeng Pan, Lixu Yan, Huan Lin, Bingbing Li, Su Yao, Shanshan Lv, Zhenwei Shi, Jinhai Mai, Jiatai Lin, Bingchao Zhao, Zeyan Xu, Zhizhen Wang, Yumeng Wang, Yuan Zhang, Huihui Wang, Chao Zhu, Chunhui Lin, Lijian Mao, Min Wu, Luwen Duan, Jingsong Zhu, Dong Hu, Zijie Fang, Yang Chen, Yongbing Zhang, Yi Li, Yiwen Zou, Yiduo Yu, Xiaomeng Li, Haiming Li, Yanfen Cui, Guoqiang Han, Yan Xu, Jun Xu, Huihua Yang, Chunming Li, Zhenbing Liu, Cheng Lu, Xin Chen, Changhong Liang, Qingling Zhang, Zaiyi Liu

According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS.

Data Augmentation Weakly supervised Semantic Segmentation +1

Global and Local Information Based Deep Network for Skin Lesion Segmentation

no code implementations • 16 Mar 2017 • Jin Qi, Miao Le, Chunming Li, Ping Zhou

In this paper, a new deep fully convolutional neural network (FCNN) is proposed to automatically segment melanoma out of skin images by end-to-end learning with only pixels and labels as inputs.

Data Augmentation Lesion Segmentation +1