Accurate and robust collagenous colitis detection histological images using features extracted from segmented area based on superpixels together with deep learning

Authors

Algimantas Kriščiukaitis (ORCID: https://orcid.org/0000-0003-4392-1937)
Dainius Jančiauskas (ORCID: https://orcid.org/0000-0002-2233-6301)
Povilas Šabanas (ORCID: https://orcid.org/0009-0009-6468-7837)
Jurgita Skiecevičienė (ORCID: https://orcid.org/0000-0002-4893-6612)
Rima Ramonaitė (ORCID: https://orcid.org/0000-0001-5093-9305)
Vytautas Kiudelis (ORCID: https://orcid.org/0000-0003-3680-1320)
Robertas Petrolis (ORCID: https://orcid.org/0000-0003-3487-733X)
Juozas Kupčinskas

Publication Details

Journal: Lithuanian University of Health Sciences
Published: 2024-10-13
Primary Topic: Microscopic Colitis
Type: article
Field-Weighted Citation Impact: 0.00

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article

Accurate and robust collagenous colitis detection histological images using features extracted from segmented area based on superpixels together with deep learning

Algimantas Kriščiukaitis, Dainius Jančiauskas, Povilas Šabanas, Jurgita Skiecevičienė et al.

Oct 13, 2024

Lithuanian University of Health Sciences

Microscopic Colitis

article

Accurate and robust collagenous colitis detection histological images using features extracted from segmented area based on superpixels together with deep learning

Algimantas Kriščiukaitis, Dainius Jančiauskas, Povilas Šabanas, Jurgita Skiecevičienė, Rima Ramonaitė, Vytautas Kiudelis, Robertas Petrolis, Juozas Kupčinskas

October 13, 2024article en

Abstract

No abstract available for this paper.

Lithuanian University of Health Sciences

Openalex Percentile: Top 42%

Microscopic Colitis

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Accurate and robust collagenous colitis detection histological images using features extracted from segmented area based on superpixels together with deep learning — Algimantas Kriščiukaitis, Dainius Jančiauskas, et al. · Lithuanian University of Health Sciences (2024) | TGRS Research Map | TGRS