This paper is in the following e-collection/theme issue:

Development and Evaluation of Research Methods, Instruments and Tools (481) Machine Learning (1465) Health Services in Resource-Poor Settings and LMICs (414) Equity and Digital Divide (218) Electronic Health Records (1053) Data Science (270) Secondary Use of Clinical Data for Research and Surveillance (367) Advanced Data Analytics in eHealth (133)

Published on in Vol 5, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20767, first published .
Using Artificial Neural Network Condensation to Facilitate Adaptation of Machine Learning in Medical Settings by Reducing Computational Burden: Model Design and Evaluation Study

Using Artificial Neural Network Condensation to Facilitate Adaptation of Machine Learning in Medical Settings by Reducing Computational Burden: Model Design and Evaluation Study

Using Artificial Neural Network Condensation to Facilitate Adaptation of Machine Learning in Medical Settings by Reducing Computational Burden: Model Design and Evaluation Study

Authors of this article:

Dianbo Liu1, 2 Author Orcid Image ;   Ming Zheng2 Author Orcid Image ;   Nestor Andres Sepulveda2 Author Orcid Image
Article Authors Cited by (3) Tweetations (3) Metrics

Journals

  1. KRISHNAN C, SCHMIDT E, ONUOHA E, MRUG M, CARDENAS C, KIM H. UNet++ Compression Techniques for Kidney and Cyst Segmentation in Autosomal Dominant Polycystic Kidney Disease. Advanced Biomedical Engineering 2024;13(0):134 View
  2. Ke Y, Yang R, Liu N. Comparing Open-Access Database and Traditional Intensive Care Studies Using Machine Learning: Bibliometric Analysis Study. Journal of Medical Internet Research 2024;26:e48330 View
  3. Chen T, Xu Z. Efficient and Flexible Method for Reducing Moderate-Size Deep Neural Networks with Condensation. Entropy 2024;26(7):567 View