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Congratulations to our 2024 Lectureship runners-up: Dr Maxx Arguilla and Dr Phillip Milner.
View Article and Find Full Text PDFTransforming Hospital Housekeeping: The Kayakalp Journey.
Glob J Qual Saf Healthc
November 2024
Department of Dentistry, All India Institute of Medical Sciences, Bhubaneswar, India.
Introduction: The Kayakalp guidelines for public healthcare facilities under the Swachh Bharat Abhiyan (Clean India Mission) focus on improving sanitation, cleanliness, and infection control at public hospitals in India. This study was conducted in a 960-bed tertiary-level teaching hospital in eastern India. Housekeeping has been a challenge in public institutions, with factors like overcrowding and resource constraints.
View Article and Find Full Text PDFReport on the AAPM grand challenge on deep generative modeling for learning medical image statistics.
Med Phys
January 2025
Dept. of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA.
Background: The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report.
Purpose: The goal of this challenge was to promote the development of deep generative models for medical imaging and to emphasize the need for their domain-relevant assessments via the analysis of relevant image statistics.
Methods: As part of this Grand Challenge, a common training dataset and an evaluation procedure was developed for benchmarking deep generative models for medical image synthesis.
Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statistics.
ArXiv
May 2024
Dept. of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, Illinois, USA.
Background: The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report.
Purpose: The goal of this challenge was to promote the development of deep generative models for medical imaging and to emphasize the need for their domain-relevant assessments via the analysis of relevant image statistics.
Methods: As part of this Grand Challenge, a common training dataset and an evaluation procedure was developed for benchmarking deep generative models for medical image synthesis.
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