Evidence map›Paper›PMID 41601790›Full record

ArticleFrontiers in medicine2025

Robust colonoscopy polyp segmentation using dynamic-Nu T-Loss with multi-scale and uncertainty-aware adaptation.

Alireza Norouziazad, Mahan Najafpour Ghazvini Fardshad, Fatemeh Esmaeildoost, Mehrdad Najafpour Ghazvini Fardshad, Razieh Salahandish

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Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Alireza NorouziazadLaboratory of Advanced Biotechnologies for Health Assessments (LAB-HA), Lassonde School of Engineering, York University, Toronto, ON, Canada.
Mahan Najafpour Ghazvini FardshadLaboratory of Advanced Biotechnologies for Health Assessments (LAB-HA), Lassonde School of Engineering, York University, Toronto, ON, Canada.
Fatemeh EsmaeildoostLaboratory of Advanced Biotechnologies for Health Assessments (LAB-HA), Lassonde School of Engineering, York University, Toronto, ON, Canada.
Mehrdad Najafpour Ghazvini FardshadLaboratory of Advanced Biotechnologies for Health Assessments (LAB-HA), Lassonde School of Engineering, York University, Toronto, ON, Canada.
Razieh SalahandishLaboratory of Advanced Biotechnologies for Health Assessments (LAB-HA), Lassonde School of Engineering, York University, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate segmentation of polyps in colonoscopy images is essential for early colorectal cancer detection; however, it remains a challenging task due to reflections, occlusions, motion artifacts, inter- and intra-polyp appearance variability, and the presence of noisy or inconsistent ground-truth annotations. In this work, we introduce dynamic-Nu T-Loss (DNA-TLoss), a robust, adaptive loss function based on the heavy-tailed Student's 𝑡-distribution that incorporates three novel extensions: (1) a per-image learnable degrees-of-freedom parameter

Indexed as

colonoscopy image analysisdeep learning in medical imagingdynamic-Nu T-Lossmulti-scale aggregationpolyp segmentationrobust loss function

Identifiers

PMID41601790
PMCPMC12832355

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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.