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

doi:10.1038/s42256-021-00305-2

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

2021-03-01

93

OA

AI

Konstantinos Balaskas

A

Alastair K. Denniston

P

Pearse A. Keane *

DOI：10.1038/s42256-021-00305-2

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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. A number of large technology companies have created code-free cloud-based platforms that allow researchers and clinicians without coding experience to create deep learning algorithms. In this study, we comprehensively analyse the performance and featureset of six platforms, using four representative cross-sectional and en-face medical imaging datasets to create image classification models. The mean (s.d.) F1 scores across platforms for all model-dataset pairs were as follows: Amazon, 93.9 (5.4); Apple, 72.0 (13.6); Clarifai, 74.2 (7.1); Google, 92.0 (5.4); MedicMind, 90.7 (9.6); Microsoft, 88.6 (5.3). The platforms demonstrated uniformly higher classification performance with the optical coherence tomography modality. Potential use cases given proper validation include research dataset curation, mobile 'edge models' for regions without internet access, and baseline models against which to compare and iterate bespoke deep learning approaches.

Keywords:

DIABETIC-RETINOPATHY

MODELS

VALIDATION

ALGORITHM

DISEASES

SYSTEM

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

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