Evidence map›Paper›PMID 41749721›Full record

ArticleBioengineering (Basel, Switzerland)2026

Inception U-Net for Enhanced Breast Ultrasound Image Segmentation Using Transfer Learning.

Yeonhyo Choi, Myoung Nam Kim, Sungdae Na

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

3 authors.

Yeonhyo ChoiDepartment of Medical & Biological Engineering, Graduate School, Kyungpook National University, Daegu 41404, Republic of Korea.
Myoung Nam KimDepartment of Biomedical Engineering, School of Medicine, Kyungpook National University, Daegu 41404, Republic of Korea.ORCID 0009-0006-4889-878X
Sungdae NaDepartment of Biomedical Engineering, Kyungpook National University Hospital, Daegu 41944, Republic of Korea.ORCID 0000-0002-9261-0492

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) No. NRF-2022R1A2C2009716 and RS-2023-00279169
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

breast ultrasounddeep learningimage segmentationinceptionmedical imagingtransfer learningU-Net

Identifiers

PMID41749721
PMCPMC12938508

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.