Výsledky vyhledávání - "Horning, Matthew P"

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Driving down Poisson error can offset classification error in clinical tasks

Autor: Delahunt, Charles B., Mehanian, Courosh, Horning, Matthew P.

Medical machine learning algorithms are typically evaluated based on accuracy vs. a clinician-defined ground truth, a reasonable initial choice since trained clinicians are usually better classifiers than ML models. However, this metric does not full

Externí odkaz: http://arxiv.org/abs/2405.06065

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How Good Are Synthetic Medical Images? An Empirical Study with Lung Ultrasound

Autor: Yu, Menghan, Kulhare, Sourabh, Mehanian, Courosh, Delahunt, Charles B, Shea, Daniel E, Laverriere, Zohreh, Shah, Ishan, Horning, Matthew P

Acquiring large quantities of data and annotations is known to be effective for developing high-performing deep learning models, but is difficult and expensive to do in the healthcare context. Adding synthetic training data using generative models of

Externí odkaz: http://arxiv.org/abs/2310.03608

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Metrics to guide development of machine learning algorithms for malaria diagnosis

Autor: Delahunt, Charles B., Gachuhi, Noni, Horning, Matthew P.

Automated malaria diagnosis is a difficult but high-value target for machine learning (ML), and effective algorithms could save many thousands of children's lives. However, current ML efforts largely neglect crucial use case constraints and are thus

Externí odkaz: http://arxiv.org/abs/2209.06947

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Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images, including Supplementary Information

Malaria is a life-threatening disease affecting millions. Microscopy-based assessment of thin blood films is a standard method to (i) determine malaria species and (ii) quantitate high-parasitemia infections. Full automation of malaria microscopy by

Externí odkaz: http://arxiv.org/abs/1908.01901

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