Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.

Contrastive Language-Vision AI Models Pretrained on Web-Scraped Multimodal Data Exhibit Sexual Objectification Bias

Autor: Wolfe, Robert, Yang, Yiwei, Howe, Bill, Caliskan, Aylin

Publikováno v: ACM FAccT 2023

Nine language-vision AI models trained on web scrapes with the Contrastive Language-Image Pretraining (CLIP) objective are evaluated for evidence of a bias studied by psychologists: the sexual objectification of girls and women, which occurs when a p

Externí odkaz: http://arxiv.org/abs/2212.11261

MAIScope: A low-cost portable microscope with built-in vision AI to automate microscopic diagnosis of diseases in remote rural settings

Autor: Sangameswaran, Rohan

According to the World Health Organization(WHO), malaria is estimated to have killed 627,000 people and infected over 241 million people in 2020 alone, a 12% increase from 2019. Microscopic diagnosis of blood cells is the standard testing procedure t

Externí odkaz: http://arxiv.org/abs/2208.06114

Hospitalization prediction from the emergency department using computer vision AI with short patient video clips.

Autor: Ip W; Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA. wui@stanford.edu., Xenochristou M; Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA., Sui E; Department of Computer Science, Stanford University, Palo Alto, CA, USA., Ruan E; Digital Health Care Integration, Stanford Health Care, Palo Alto, CA, USA., Ribeira R; Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, USA., Dash D; Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, CA, USA., Srinivasan M; Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA., Artandi M; Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA., Omiye JA; Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA.; Department of Dermatology, Stanford University School of Medicine, Palo Alto, CA, USA., Scoulios N; Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA., Hofmann HL; Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA., Mottaghi A; Department of Electrical Engineering, Stanford University, Palo Alto, CA, USA., Weng Z; Institute for Computational & Mathematical Engineering, Stanford University, Palo Alto, CA, USA., Kumar A; Department of Computer Science, Stanford University, Palo Alto, CA, USA., Ganesh A; Department of Computer Science, Stanford University, Palo Alto, CA, USA., Fries J; Stanford Center for Biomedical Informatics Research, Palo Alto, CA, USA., Yeung-Levy S; Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA.; Department of Computer Science, Stanford University, Palo Alto, CA, USA.; Department of Electrical Engineering, Stanford University, Palo Alto, CA, USA.; Clinical Excellence Research Center, Stanford University School of Medicine, Palo Alto, CA, USA.; Chan Zuckerberg Biohub-San Francisco, San Francisco, CA, USA., Hofmann LV; Digital Health Care Integration, Stanford Health Care, Palo Alto, CA, USA.; Department of Radiology, Stanford University School of Medicine, Palo Alto, CA, USA.

Publikováno v: NPJ digital medicine [NPJ Digit Med] 2024 Dec 19; Vol. 7 (1), pp. 371. Date of Electronic Publication: 2024 Dec 19.

Python deep learning : next generation techniques to revolutionize computer vision, AI, speech and data analysis / Valentino Zocca [and three others]. [electronic resource]

Externí odkaz: Kolekce e-knih KNAV Registrovani uzivatele: plny text online 5 minut, dalsi pristup na vyzadani. Registered users: full text online 5 minutes, further access on request.