Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer-Reference-Cited by-同舟云学术

Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer

Published:2021-02 Issue: Volume:68 Page:101915
ISSN:1361-8415
Container-title:Medical Image Analysis
language:en
Short-container-title:Medical Image Analysis

Author:

Thomas Simon M.^ORCID,Lefevre James G.^ORCID,Baxter Glenn^ORCID,Hamilton Nicholas A.

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference49 articles.

1. TensorFlow: A system for large-scale machine learning, in: Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation;Abadi;OSDI 2016,2016

2. Not just digital pathology, intelligent digital pathology;Acs;JAMA Oncol.,2018

3. Sanity checks for saliency maps;Adebayo,2018

4. An unsupervised feature learning framework for basal cell carcinoma image analysis;Arevalo;Artif. Intell. Med.,2015

5. Skin cancer in Australia,2016

Cited by 102 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. Reply-letter to the editor;Clinical Nutrition;2024-09

2. 3D U-Net-Norm architecture for improving generalization of BraTS images;Multimedia Tools and Applications;2024-08-02

3. Two-dimensional hybrid incremental learning (2DHIL) framework for semantic segmentation of skin tissues;Image and Vision Computing;2024-08

4. Two-dimensional hybrid incremental learning (2DHIL) framework for semantic segmentation of skin tissues;Image and Vision Computing;2024-08

5. SkinNet-14: a deep learning framework for accurate skin cancer classification using low-resolution dermoscopy images with optimized training time;Neural Computing and Applications;2024-08-01