ArticleJournal of healthcare informatics research2026
MiniGPT-Pancreas: Multimodal Large Language Model for Pancreas Cancer Observation and Localization in CT Images.
Article in Journal of healthcare informatics research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pancreatic cancer remains one of the deadliest malignancies, primarily because of its subtle CT appearance and frequent late-stage diagnosis. We introduce MiniGPT-Pancreas, a lightweight multimodal large language model (MLLM) that interprets natural-language queries within an interactive ChatGPT-style interface, as well as computed tomography images, and returns precise bounding-box predictions for the pancreas and associated tumors. A cascaded fine-tuning strategy was applied to MiniGPTv2, a multi-task general-purpose MLLM, with a focus on pancreas and tumor detection, using the National Institute of Health (NIH) and Medical Segmentation Decathlon (MSD) pancreas datasets. Pancreas detection achieved an average intersection over Union (IoU) of 0.57 on NIH and MSD datasets, outperforming the base MiniGPT-Pancreas model and more recent MLLMs like GLM-4.1V-9B-Base (general-purpose) and UMIT (specific to the biomedical domain). Tumor observation on MSD yielded an accuracy, precision, recall, and F1 score of all about 0.87, surpassing MiniGPT-v2, GLM-4.1V-9B-Base, and UMIT. For tumor localization, the IoU was 0.28, higher than UMIT (IoU=0.07), but lower than GLM-4.1V-9B-Base (IoU=0.48). On multi-organ detection on the AbdomenCT-1k dataset, MiniGPT-Pancreas outperformed GLM-4.1V-9B-Base and UMIT in all organs, with an IoU of 0.50 on pancreas vs. 0.43 and 0.03, respectively. MiniGPT-Pancreas was rated highly by an international group of 10 expert general surgeons (Italy, Singapore, and the UK) as a potential training tool, especially for verification (4.5/5.0), and training of young specialists (4.5/5.0) on a 5-point Likert scale. While operating on 2D slices limits volumetric context, MiniGPT-Pancreas demonstrates that compact MLLMs can rival specialized vision networks in pancreas imaging, offering an intuitive, language-driven tool for AI-assisted radiology. The code is publicly available at https://github.com/elianastasio/MiniGPTPancreas.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.