Evidence map›Paper›PMID 41267884›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2025

Masked image modeling in medical hyperspectral imaging: reconstruction evaluation and downstream tasks.

Kelden Pruitt, Hemanth Pasupuleti, James Yu, Weston DeAtley, Baowei Fei

Abstract read
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Kelden PruittUniversity of Texas at Dallas, Department of Bioengineering, Richardson, TX.
Hemanth PasupuletiUniversity of Texas at Dallas, Department of Bioengineering, Richardson, TX.
James YuUniversity of Texas at Dallas, Department of Bioengineering, Richardson, TX.
Weston DeAtleyUniversity of Texas at Dallas, Department of Bioengineering, Richardson, TX.
Baowei FeiUniversity of Texas at Dallas, Department of Bioengineering, Richardson, TX.

Funding

ACADEMIC-INDUSTRIAL PARTNERSHIP FOR TRANSLATION OF PET/TRUS GUIDED INTERVENTIONR01CA204254 · NCI · UNIVERSITY OF TEXAS DALLAS · PI FEI, BAOWEI · 2017 to 2021
$2.0M
A Real-Time Hyperspectral Laparoscopic Stereo Imaging System for Robot-Assisted SurgeryR01CA288379 · NCI · UNIVERSITY OF TEXAS DALLAS · PI BAOWEI FEI · 2024 to 2026
$1.6M
NCI NIH HHS R01 CA204254NCI NIH HHS R01 CA288379
6 · The paper itself

Abstract

Self-supervised pre-training has been shown to improve deep learning networks in various tasks including natural language processing and computer vision. While this approach has shown promise in various fields, more development and translation need to be dedicated to medical imaging applications. Current literature scarcely focuses on thorough assessment of implemented pre-training approaches as well, potentially hindering performance in downstream tasks. In this work, we leverage a state-of-the-art pre-training architecture with hyperspectral imaging (HSI) to effectively encode spatial and spectral features of various

Indexed as

abdominal tissuedeep learningHyperspectral imagingmasked autoencoding

Identifiers

PMID41267884
PMCPMC12629530

What Socratic holds

Textmetadata
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Registered trials

None linked

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 Socratic graph.