ArticleThe Analyst2017
SIproc: an open-source biomedical data processing platform for large hyperspectral images.
Article in The Analyst, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Medical hyperspectral imaging: an updated review of technology advancements and biomedical applications.Journal of biomedical optics · 2026Pooled it
- Leveraging mid-infrared spectroscopic imaging and deep learning for tissue subtype classification in ovarian cancer.The Analyst · 2023Article
- Compact and ultracompact spectral imagers: technology and applications in biomedical imaging.Journal of biomedical optics · 2023Review
- Article
- Noise-free simulation of an FT-IR imaging hyperspectral dataset of pancreatic biopsy core bound by experiment.Scientific data · 2019Article
- Digital Staining of High-Definition Fourier Transform Infrared (FT-IR) Images Using Deep Learning.Applied spectroscopy · 2019Article
- Deep learning for FTIR histology: leveraging spatial and spectral features with convolutional neural networks.The Analyst · 2019Article
- Comparison of spectral and spatial denoising techniques in the context of High Definition FT-IR imaging hyperspectral data.Scientific reports · 2018Article
- Application of Vibrational Spectroscopy and Imaging to Point-of-Care Medicine: A Review.Applied spectroscopy · 2018Review
- Infrared Spectroscopic Imaging Advances as an Analytical Technology for Biomedical Sciences.Analytical chemistry · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 6 countries.
Funding
Abstract
There has recently been significant interest within the vibrational spectroscopy community to apply quantitative spectroscopic imaging techniques to histology and clinical diagnosis. However, many of the proposed methods require collecting spectroscopic images that have a similar region size and resolution to the corresponding histological images. Since spectroscopic images contain significantly more spectral samples than traditional histology, the resulting data sets can approach hundreds of gigabytes to terabytes in size. This makes them difficult to store and process, and the tools available to researchers for handling large spectroscopic data sets are limited. Fundamental mathematical tools, such as MATLAB, Octave, and SciPy, are extremely powerful but require that the data be stored in fast memory. This memory limitation becomes impractical for even modestly sized histological images, which can be hundreds of gigabytes in size. In this paper, we propose an open-source toolkit designed to perform out-of-core processing of hyperspectral images. By taking advantage of graphical processing unit (GPU) computing combined with adaptive data streaming, our software alleviates common workstation memory limitations while achieving better performance than existing applications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.