Evidence map›Paper›PMID 41031080›Full record

ArticleArXiv2025

Text Embedded Swin-UMamba for DeepLesion Segmentation.

Ruida Cheng, Tejas Sudharshan Mathai, Pritam Mukherjee, Benjamin Hou, Qingqing Zhu, Zhiyong Lu, Matthew McAuliffe, Ronald M Summers

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 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

8 authors.

Ruida ChengScientific Application Services, Center of Information Technology, NIH.
Tejas Sudharshan MathaiImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, NIH.
Pritam MukherjeeImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, NIH.
Benjamin HouDivision of Intramural Research (DIR), National Library of Medicine (NLM), NIH.
Qingqing ZhuDivision of Intramural Research (DIR), National Library of Medicine (NLM), NIH.
Zhiyong LuDivision of Intramural Research (DIR), National Library of Medicine (NLM), NIH.
Matthew McAuliffeScientific Application Services, Center of Information Technology, NIH.
Ronald M SummersImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, NIH.

Funding

Computer Aided Detection for Radiologic ImagesZ01CL040004 · CLC · CLINICAL CENTER · PI SUMMERS, RONALD M. · 2003 to 2008
$41k
Intramural NIH HHS Z01 CL040004
6 · The paper itself

Abstract

Segmentation of lesions on CT enables automatic measurement for clinical assessment of chronic diseases (e.g., lymphoma). Integrating large language models (LLMs) into the lesion segmentation workflow offers the potential to combine imaging features with descriptions of lesion characteristics from the radiology reports. In this study, we investigate the feasibility of integrating text into the Swin-UMamba architecture for the task of lesion segmentation. The publicly available ULS23 DeepLesion dataset was used along with short-form descriptions of the findings from the reports. On the test dataset, a high Dice Score of 82 ± 18% and low Hausdorff distance of 6.58 ± 10.64 (pixels) was obtained for lesion segmentation. The proposed Text-Swin-UMamba model outperformed prior approaches: 37% improvement over the LLM-driven LanGuideMedSeg model (

Indexed as

DeepLesionUniversal Lesion Segmentation

Identifiers

PMID41031080
PMCPMC12478426

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.