Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images-Reference-Cited by-同舟云学术

Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images

Published:2019-02 Issue: Volume:86 Page:188-200
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Hou Le^ORCID,Nguyen Vu,Kanevsky Ariel B.,Samaras Dimitris,Kurc Tahsin M.,Zhao Tianhao,Gupta Rajarsi R.^ORCID,Gao Yi,Chen Wenjin,Foran David,Saltz Joel H.

Funder

National Cancer Institute

U.S. National Library of Medicine

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference74 articles.

1. Mdnet: a semantically and visually interpretable medical image diagnosis network;Zhang,2017

2. Tandemnet: distilling knowledge from medical images using diagnostic reports as optional semantic references;Zhang,2017

3. Cancer diagnosis by nuclear morphometry using spatial information;Huang;Pattern Recognit. Lett.,2014

4. Automated analysis and diagnosis of skin melanoma on whole slide histopathological images;Lu;Pattern Recognit.,2015

5. Review of the current state of whole slide imaging in pathology;Pantanowitz;J. Pathol. Inform.,2011

Cited by 121 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A robust image segmentation and synthesis pipeline for histopathology;Medical Image Analysis;2025-01

2. DB-FCN: An end-to-end dual-branch fully convolutional nucleus detection model;Expert Systems with Applications;2024-12

3. A multi-resolution self-supervised learning framework for semantic segmentation in histopathology;Pattern Recognition;2024-11

4. A-ReSEUnet: Achieve no-label binary segmentation of nuclei in histology images;Knowledge-Based Systems;2024-10

5. Cell comparative learning: A cervical cytopathology whole slide image classification method using normal and abnormal cells;Computerized Medical Imaging and Graphics;2024-10