3 research outputs found
Hyper-Skin: A Hyperspectral Dataset for Reconstructing Facial Skin-Spectra from RGB Images
We introduce Hyper-Skin, a hyperspectral dataset covering wide range of
wavelengths from visible (VIS) spectrum (400nm - 700nm) to near-infrared (NIR)
spectrum (700nm - 1000nm), uniquely designed to facilitate research on facial
skin-spectra reconstruction. By reconstructing skin spectra from RGB images,
our dataset enables the study of hyperspectral skin analysis, such as melanin
and hemoglobin concentrations, directly on the consumer device. Overcoming
limitations of existing datasets, Hyper-Skin consists of diverse facial skin
data collected with a pushbroom hyperspectral camera. With 330 hyperspectral
cubes from 51 subjects, the dataset covers the facial skin from different
angles and facial poses. Each hyperspectral cube has dimensions of
10241024448, resulting in millions of spectra vectors per
image. The dataset, carefully curated in adherence to ethical guidelines,
includes paired hyperspectral images and synthetic RGB images generated using
real camera responses. We demonstrate the efficacy of our dataset by showcasing
skin spectra reconstruction using state-of-the-art models on 31 bands of
hyperspectral data resampled in the VIS and NIR spectrum. This Hyper-Skin
dataset would be a valuable resource to NeurIPS community, encouraging the
development of novel algorithms for skin spectral reconstruction while
fostering interdisciplinary collaboration in hyperspectral skin analysis
related to cosmetology and skin's well-being. Instructions to request the data
and the related benchmarking codes are publicly available at:
\url{https://github.com/hyperspectral-skin/Hyper-Skin-2023}.Comment: Skin spectral datase
The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection
Artificial intelligence represents a new frontier in human medicine that
could save more lives and reduce the costs, thereby increasing accessibility.
As a consequence, the rate of advancement of AI in cancer medical imaging and
more particularly tissue pathology has exploded, opening it to ethical and
technical questions that could impede its adoption into existing systems. In
order to chart the path of AI in its application to cancer tissue imaging, we
review current work and identify how it can improve cancer pathology
diagnostics and research. In this review, we identify 5 core tasks that models
are developed for, including regression, classification, segmentation,
generation, and compression tasks. We address the benefits and challenges that
such methods face, and how they can be adapted for use in cancer prevention and
treatment. The studies looked at in this paper represent the beginning of this
field and future experiments will build on the foundations that we highlight