Short-Wavelength Infrared Hyperspectral Imaging for Biomedical Applications

Randeberg, Lise Lyngsnes; Catelli, Emilio; Hernández, Julio César Morales

doi:10.1117/3.2604326.ch5

Cited by 2 publications

(2 citation statements)

References 142 publications

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“…Capturing Spectral WISs with a Hyperspectral Camera: A polarimetric Short-wave infrared (SWIR) hyperspectral imaging system (HySpex: SWIR-384, Norsk Elektro Optikk AS, Norway) similar to that used in previous studies [11,33,65] was used to image the hover fly wings. The hyperspectral camera covered the shortwave infrared wavelength range from 0.95 to 2.5 μm and a spatial resolution of 240 μm/pix, with 288 spectral bands.…”

Section: Methodsmentioning

confidence: 99%

Discrimination of Hover Fly Species and Sexes by Wing Interference Signals

Li,

Runemark,

Hernandez

et al. 2023

Advanced Science

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Remote automated surveillance of insect abundance and diversity is poised to revolutionize insect decline studies. The study reveals spectral analysis of thin‐film wing interference signals (WISs) can discriminate free‐flying insects beyond what can be accomplished by machine vision. Detectable by photonic sensors, WISs are robust indicators enabling species and sex identification. The first quantitative survey of insect wing thickness and modulation through shortwave‐infrared hyperspectral imaging of 600 wings from 30 hover fly species is presented. Fringy spectral reflectance of WIS can be explained by four optical parameters, including membrane thickness. Using a Naïve Bayes Classifier with five parameters that can be retrieved remotely, 91% is achieved accuracy in identification of species and sexes. WIS‐based surveillance is therefore a potent tool for remote insect identification and surveillance.

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Section: Methodsmentioning

confidence: 99%

Discrimination of Hover Fly Species and Sexes by Wing Interference Signals

Li,

Runemark,

Hernandez

et al. 2023

Advanced Science

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show abstract

“…As described in [27] and mentioned by [28], a workshop was held in 2018 to develop a roadmap for foundational research on artificial intelligence in medical imaging [27]. This workshop reviewed the state of the art, identified knowledge gaps five key research priorities for application of artificial intelligence in medicine, which are as follows:…”

Section: Literature Reviewmentioning

confidence: 99%

Common pitfalls in using AI in high-risk domains

Randeberg,

Wesenberg

2024

Photonics in Dermatology and Plastic Surgery 2024

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Deep learning, AI and machine learning are emerging as important tools e.g., to segment, classify and detect pathologies medical diagnostics. Powerful and easy to use frameworks for machine learning have increased the accessibility of these methods. At the same time this removes the need for machine learning experts with deep insights in the limitations of the technology and places the power of AI in the hands of domain experts. Although such high-risk domains are very diverse, the challenges using machine learning are related. This paper will discuss insights from application of machine learning in domains as diverse as medical image analysis, and conditionbased monitoring in the Norwegian Oil and gas industry. We then discuss how these insights can be applied when analyzing hyperspectral data from human skin.

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Short-Wavelength Infrared Hyperspectral Imaging for Biomedical Applications

Cited by 2 publications

References 142 publications

Discrimination of Hover Fly Species and Sexes by Wing Interference Signals

Discrimination of Hover Fly Species and Sexes by Wing Interference Signals

Common pitfalls in using AI in high-risk domains

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