LMeRAN: Label Masking-Enhanced Residual Attention Network for Multi-Label Chest X-Ray Disease Aided Diagnosis.

Hongping Fu, Chao Song, Xiaolong Qu, Dongmei Li, Lei Zhang

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 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.

Hongping FuSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, China.

Chao SongSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, China.ORCID 0009-0000-9499-1105

Xiaolong QuSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, China.ORCID 0000-0003-0932-0361

Dongmei LiSchool of Information Science and Technology, Beijing Forestry University, Beijing 100083, China.ORCID 0000-0001-7409-6656

Lei ZhangNational Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100083, China.ORCID 0000-0003-0529-1305

Funding

Fundamental Research Funds for the Central Universities BLX2016-24Natural Science Foundation of China 61702038

6 · The paper itself

Abstract

Chest X-ray (CXR) imaging is essential for diagnosing thoracic diseases, and computer-aided diagnosis (CAD) systems have made substantial progress in automating the interpretation of CXR images. However, some existing methods often overemphasize local features while neglecting global context, limiting their ability to capture the broader pathological landscape. Moreover, most methods fail to model label correlations, leading to insufficient utilization of prior knowledge. To address these limitations, we propose a novel multi-label CXR image classification framework, termed the

Indexed as

Diagnosis, Computer-AssistedRadiographic Image Interpretation, Computer-AssistedRadiography, ThoracicAlgorithmsHumansNeural Networks, ComputerX-Raysattention mechanismchest X-raycomputer-aided diagnosislabel mask trainingmulti-label classification

Identifiers

PMID41012916

DOI10.3390/s25185676

PMCPMC12473221

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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.