Name
Title | ||
|---|---|---|
MobileDeepPill: A Small-Footprint Mobile Deep Learning System for Recognizing Unconstrained Pill Images. |
Abstract | ||
|---|---|---|
Correct identification of prescription pills based on their visual appearance is a key step required to assure patient safety and facilitate more effective patient care. With the availability of high-quality cameras and computational power on smartphones, it is possible and helpful to identify unknown prescription pills using smartphones. Towards this goal, in 2016, the U.S. National Library of Medicine (NLM) of the National Institutes of Health (NIH) announced a nationwide competition, calling for the creation of a mobile vision system that can recognize pills automatically from a mobile phone picture under unconstrained real-world settings. In this paper, we present the design and evaluation of such mobile pill image recognition system called MobileDeepPill. The development of MobileDeepPill involves three key innovations: a triplet loss function which attains invariances to real-world noisiness that deteriorates the quality of pill images taken by mobile phones; a multi-CNNs model that collectively captures the shape, color and imprints characteristics of the pills; and a Knowledge Distillation-based deep model compression framework that significantly reduces the size of the multi-CNNs model without deteriorating its recognition performance. Our deep learning-based pill image recognition algorithm wins the First Prize (champion) of the NIH NLM Pill Image Recognition Challenge. Given its promising performance, we believe MobileDeepPill helps NIH tackle a critical problem with significant societal impact and will benefit millions of healthcare personnel and the general public. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3081333.3081336 | MobiSys |
Keywords | Field | DocType |
Mobile Deep Learning Systems,Unconstrained Pill Image Recognition,Deep Neural Network Model Compression,Mobile Health | Patient safety,Computer science,Pill,Real-time computing,Human–computer interaction,Artificial intelligence,Mobile phone,Deep learning,Health care,Simulation,Societal impact of nanotechnology,Champion,Visual appearance | Conference |
Citations | PageRank | References |
18 | 0.89 | 14 |
Authors | ||
3 | ||