The Journal of Infectious Diseases · 2023 · 24 citations · 102 references
Veterinary MedicineInfectious DiseasesArtificial IntelligenceBiomedical ImagingDiagnostic ImagingComputer-aided DiagnosisDisease ModelingInfectious Disease DetectionMedical Image AnalysisMedical ImagingAnimal Disease ModelsInfectious Disease DiagnosticsInfectious Disease Imaging
The mass production of the graphics processing unit and the coronavirus disease 2019 (COVID-19) pandemic have provided the means and the motivation, respectively, for rapid developments in artificial intelligence (AI) and medical imaging techniques. This has led to new opportunities to improve patient care but also new challenges that must be overcome before these techniques are put into practice. In particular, early AI models reported high performances but failed to perform as well on new data. However, these mistakes motivated further innovation focused on developing models that were not only accurate but also stable and generalizable to new data. The recent developments in AI in response to the COVID-19 pandemic will reap future dividends by facilitating, expediting, and informing other medical AI applications and educating the broad academic audience on the topic. Furthermore, AI research on imaging animal models of infectious diseases offers a unique problem space that can fill in evidence gaps that exist in clinical infectious disease research. Here, we aim to provide a focused assessment of the AI techniques leveraged in the infectious disease imaging research space, highlight the unique challenges, and discuss burgeoning solutions.
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DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2023 · 76.3K citations · Full text
Learning Deep Features for Discriminative Localization
Bolei Zhou, Aditya Khosla, Àgata Lapedriza et al. · 2016 · 10.8K citations · Full text
Bruce Fischl, David H. Salat, Evelina Busa et al. · Neuron · 2002 · 9K citations · Full text