AICOM-MP: an AI-based monkeypox detector for resource-constrained environments

被引:3
|
作者
Yang, Tianyi [1 ]
Yang, Tianze [1 ]
Liu, Andrew [1 ]
An, Na [2 ]
Liu, Shaoshan [3 ,4 ]
Liu, Xue [1 ]
机构
[1] McGill Univ, Sch Comp Sci, Montreal, PQ, Canada
[2] Harvard TH Chan Sch Publ Hlth, Boston, MA USA
[3] WellAging, Santa Clara, CA USA
[4] 220 E Warren Cmn, Fremont, CA 94539 USA
关键词
Health AI; monkeypox; autonomous mobile clinics; SDG3; VIRUS;
D O I
10.1080/09540091.2024.2306962
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Under the Autonomous Mobile Clinics (AMCs) initiative, the AI Clinics on Mobile (AICOM) project is developing, open sourcing, and standardising health AI technologies on low-end mobile devices to enable health-care access in least-developed countries (LDCs). As the first step, we introduce AICOM-MP, an AI-based monkeypox detector specially aiming for handling images taken from resource-constrained devices. We have developed AICOM-MP with the following principles: minimisation of gender, racial, and age bias; ability to conduct binary classification without over-relying on computing power; capacity to produce accurate results irrespective of images' background, resolution, and quality. AICOM-MP has achieved state-of-the-art (SOTA) performance. We have hosted AICOM-MP as a web service to allow universal access to monkeypox screening technology, and open-sourced both the source code and the dataset of AICOM-MP to allow health AI professionals to integrate AICOM-MP into their services.
引用
收藏
页数:14
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