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Hyperspectral Imaging

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Hyperspectral imaging facilitating resect-and-discard strategy through artificial intelligence-assisted diagnosis of colorectal polyps: A pilot study.

Cancer medicine
BACKGROUND AND AIMS: The resect-and-discard strategy for colorectal polyps based on accurate optical diagnosis remains challenges. Our aim was to investigate the feasibility of hyperspectral imaging (HSI) for identifying colorectal polyp properties a...

Detection of hospital super bacteria MRSA from the hands of healthcare professionals using machine learning and hyperspectral imaging.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Healthcare-associated infections resulting from cross-contamination, particularly from the hands of multidisciplinary staff, significantly impact patient mortality in health units. The prolonged nature of classical phenotypic diagnostic methods under...

Machine and Deep Learning in Hyperspectral Fluorescence-Guided Brain Tumor Surgery.

Advances in experimental medicine and biology
Malignant glioma resection is often the first line of treatment in neuro-oncology. During glioma surgery, the discrimination of tumor's edges can be challenging at the infiltration zone, even by using surgical adjuncts such as fluorescence guidance (...

How to Collect and Interpret Medical Pictures Captured in Highly Challenging Environments that Range from Nanoscale to Hyperspectral Imaging.

Current medical imaging
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 por...

Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging.

Journal of biomedical optics
SIGNIFICANCE: Hyperspectral reflectance imaging can be used in medicine to identify tissue types, such as tumor tissue. Tissue classification algorithms are developed based on, e.g., machine learning or principle component analysis. For the developme...

Single-shot hyperspectral imaging based on dual attention neural network with multi-modal learning.

Optics express
Hyperspectral imaging is being extensively investigated owing to its promising future in critical applications such as medical diagnostics, sensing, and surveillance. However, current techniques are complex with multiple alignment-sensitive component...

Intelligent identification of film on cotton based on hyperspectral imaging and convolutional neural network.

Science progress
The identification of the film on cotton is of great significance for the improvement of cotton quality. Most of the existing technologies are dedicated to removing colored foreign fibers from cotton using photoelectric sorting methods. However, the ...

[Application and prospects of hyperspectral imaging and deep learning in traditional Chinese medicine in context of AI and industry 4.0].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica
In the 21 st century, the rise of artificial intelligence(AI) marks the arrival of the intelligence era or the era of Industry 4.0. In addition to the rapid development of computer and electronic information science, machine learning, as the core int...

Classification, identification, and growth stage estimation of microalgae based on transmission hyperspectral microscopic imaging and machine learning.

Optics express
A transmission hyperspectral microscopic imager (THMI) that utilizes machine learning algorithms for hyperspectral detection of microalgae is presented. The THMI system has excellent performance with spatial and spectral resolutions of 4 µm and 3 nm,...