Title:How to Collect and Interpret Medical Pictures Captured in Highly
Challenging Environments that Range from Nanoscale to Hyperspectral
Imaging
Volume: 20
Author(s): Asif A. Laghari*, Vania V. Estrela*Shoulin Yin
Affiliation:
- Department of Computer Science, Sindh Madressatul Islam University, Karachi, Pakistan
- Departamento de Engenharia de Telecomunica, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
Keywords:
Medical imaging, Visualization, Cyber-physical systems, PACS, Content-based image retrieval, Virtual reality (VR), Augmented reality (AR), Public health, Data imputation.
Abstract: Digital well-being records are multimodal and high-dimensional (HD). Better theradiagnostics stem from new computationally thorough and edgy
technologies, i.e., hyperspectral (HSI) imaging, super-resolution, and nanoimaging, but advance mess data portrayal and retrieval. A patient's state
involves multiple signals, medical imaging (MI) modalities, clinical variables, dialogs between clinicians and patients, metadata, genome
sequencing, and signals from wearables. Patients' high volume, personalized data amassed over time have advanced artificial intelligence (AI)
models for higher-precision inferences, prognosis, and tracking. AI promises are undeniable, but with slow spreading and adoption, given partly
unstable AI model performance after real-world use. The HD data is a rate-limiting factor for AI algorithms generalizing real-world scenarios. This
paper studies many health data challenges to robust AI models' growth, aka the dimensionality curse (DC). This paper overviews DC in the MIs'
context, tackles the negative out-of-sample influence and stresses important worries for algorithm designers. It is tricky to choose an AI platform
and analyze hardships. Automating complex tasks requires more examination. Not all MI problems need automation via DL. AI developers spend
most time refining algorithms, and quality data are crucial. Noisy and incomplete data limits AI, requiring time to handle control, integration, and
analyses. AI demands data mixing skills absent in regular systems, requiring hardware/software speed and flexible storage. A partner or service can
fulfill anomaly detection, predictive analysis, and ensemble modeling.
