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Code-free deep learning for multi-modality medical image classification

Several technology companies offer platforms for users without coding experience to develop deep learning algorithms. This Analysis compares the performance of six ‘code-free deep learning’ platforms (from Amazon, Apple, Clarifai, Google, MedicMind and Microsoft) in creating medical image classification models.

Edward Korot
Zeyu Guan
Pearse A. Keane
ResearchOpen Access01 Mar 2021
Nature Machine Intelligence

Volume: 3, P: 288-298
AI for medical imaging goes deep

An artificial intelligence (AI) using a deep-learning approach can classify retinal images from optical coherence tomography for early diagnosis of retinal diseases and has the potential to be used in other image-based medical diagnoses.

Daniel S. W. Ting
Yong Liu
Tien Y. Wong
News & Views07 May 2018
Nature Medicine

Volume: 24, P: 539-540
End-to-end privacy preserving deep learning on multi-institutional medical imaging

Gaining access to medical data to train AI applications can present problems due to patient privacy or proprietary interests. A way forward can be privacy-preserving federated learning schemes. Kaissis, Ziller and colleagues demonstrate here their open source framework for privacy-preserving medical image analysis in a remote inference scenario.

Georgios Kaissis
Alexander Ziller
Rickmer Braren
Research24 May 2021
Nature Machine Intelligence

Volume: 3, P: 473-484
A comparative study of an on premise AutoML solution for medical image classification

Kabilan Elangovan
Gilbert Lim
Daniel Ting
ResearchOpen Access07 May 2024
Scientific Reports

Volume: 14, P: 1-11
Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images

Deep-learning models trained on retinal fundus images can be used to identify chronic kidney disease and type 2 diabetes and to predict the risk of the progression of these diseases.

Kang Zhang
Xiaohong Liu
Guangyu Wang
Research15 Jun 2021
Nature Biomedical Engineering

Volume: 5, P: 533-545
Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis

Ravi Aggarwal
Viknesh Sounderajah
Ara Darzi
ReviewsOpen Access07 Apr 2021
npj Digital Medicine

Volume: 4, P: 1-23
Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images

Li et al. develop a cascaded deep learning system for automated retinal detachment and macular status detection based on ultra-widefield fundus (UWF) images. With reliable and comparable performance to an experienced opthamologist, this system can also provide guidance to patients regarding appropriate preoperative posturing to reduce RD progression.

Zhongwen Li
Chong Guo
Haotian Lin
ResearchOpen Access08 Jan 2020
Communications Biology

Volume: 3, P: 1-10
Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence

A natural language processing system can support physicians in diagnostic assessments by extracting clinical information from electronic medical records to accurately predict diagnosis in pediatric patients.

Huiying Liang
Brian Y. Tsui
Huimin Xia
Research11 Feb 2019
Nature Medicine

Volume: 25, P: 433-438

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