HAMIL: High-Resolution Activation Maps and Interleaved Learning for Weakly Supervised Segmentation of Histopathological Images
Author:
Affiliation:
1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
2. Department of Pathology, West China Second University Hospital, Sichuan University, Chengdu, China
Funder
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software
Link
http://xplorestaging.ieee.org/ielx7/42/10269089/10107649.pdf?arnumber=10107649
Reference60 articles.
1. Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images
2. SenseCare: A research platform for medical image informatics and interactive 3D visualization;duan;arXiv 2004 07031,2020
3. BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation
4. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
5. Semi-supervised Histological Image Segmentation via Hierarchical Consistency Enforcement
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