Evidence map›Paper›PMID 41912958›Full record

ArticleJournal of imaging informatics in medicine2026

Zero-Shot Lung Disease Detection Using Radiological Symptomatic Descriptors and Pretrained Neural Networks.

Sabbir Ahmed, Md Abdul Hamid, Muhammad Mostafa Monowar, Mohammad Abu Yousuf

Abstract read
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In one paragraph

Article in Journal of imaging informatics in medicine, 2026. 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

4 authors.

Sabbir AhmedErik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas, Richardson, TX, USA.ORCID https://orcid.org/0000-0003-1186-4755
Md Abdul HamidDepartment of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah, 21589, Kingdom of Saudi Arabia.
Muhammad Mostafa MonowarDepartment of Information Technology, Faculty of Computing & Information Technology, King Abdulaziz University, Jeddah, 21589, Kingdom of Saudi Arabia.
Mohammad Abu YousufInstitute of Information Technology, Jahangirnagar University, Dhaka, 1342, Bangladesh. yousuf@juniv.edu.

Funding

Deanship of Scientific Research (DSR), King abdulaziz University PIP: 1810-611-2024
6 · The paper itself

Abstract

Aligning radiological features with clinical text descriptions remains a key challenge for zero-shot disease recognition in chest radiography. We propose DVLM (Dual-Head Vision-Language Model with Neural Memory), a framework combining Vision Transformer visual encoding with ClinicalBERT-based text processing through parallel contrastive and supervised learning branches. A neural memory module stores disease-relevant patterns during training for improved generalization to unseen pathologies. We evaluated DVLM on CheXpert, MIMIC-CXR, and PadChest using multi-seed validation (five seeds

Indexed as

Contrastive learningNeural memoryVision-language modelZero-shot

Identifiers

PMID41912958

What OpenQuestion holds

Textmetadata
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.