arXiv subjects

Sachin Acharya

Publications and source records attributed to Sachin Acharya.

3 recordsLinked to original sources

arXiv2026

SAGE: An Expert-Annotated South Asian GI Endoscopy Dataset for Multimodal Learning and Hallucination Analysis

Gastrointestinal cancers represent a growing health burden in the South Asian region, driven largely by rapid changes in socio-economic conditions and lifestyle habits. However, early diagnosis remains limited by inadequate equipment, financial resources, and scarce GI expertise. AI-assisted diagnosis and report generation show great promise in alleviating this problem by providing non-specialist healthcare workers the technical expertise to perform diagnosis. Yet, almost all open-source, publicly available datasets are predominantly collected from the European region, with minimal representation from the South Asian region. The lack of open-source GI datasets from diverse geographic regions has made it difficult to assess whether population bias is present in existing models, and to develop geographically inclusive AI tools for automated GI diagnosis. To address this gap, we introduce SAGE: An Expert-Annotated South Asian GI Endoscopy dataset for Multimodal Learning and Hallucination Analysis, for image captioning, multi-label classification, and VQA tasks. It consists of 1,300 images, their captions along with hallucination tag, 18 labels and 14,276 question-answer pairs making it well-suited for diverse range of tasks including classification, benchmarking, and fine-tuning LMMs. We further conducted benchmarking of task-specific models, like multi-class classifiers on the effect of population shift which reveals that such models suffers the most with an average F1 score drop of 54 points on South Asian dataset. Further, benchmarking of contemporary LMMs reveals a substantial drop in the average GREEN score for anatomical landmark detection (0.308) and abnormality detection (0.410). We open-source our dataset under the CC BY-SA 4.0 license, and we hope to encourage others to contribute toward more inclusive dataset representation and help counteract population bias in medical AI.

cs.CV↗

arXiv2026

A Benchmark for Hallucination Detection in VLMs for Gastrointestinal Endoscopy

Vision-language models (VLMs) are prone to hallucination, which remains a major barrier to their safe deployment in clinical practice. To date, most hallucination detection methods have been evaluated on radiology benchmarks such as MIMIC-CXR and VQA-RAD, while gastrointestinal (GI) endoscopy remains largely underexplored. In this paper, we benchmark nine hallucination detection methods on the Gut-VLM dataset, a GI diagnostic Visual Question Answering (VQA) dataset with 4,392 test VQA pairs, across five VLMs (MedGemma-4B, MedGemma-27B, LLaVA-Med-7B, LLaVA-v1.6-7B, and Lingshu-32B). The methods span three categories: black-box methods (RadFlag, SelfCheckGPT-NLI), gray-box methods (AvgProb, AvgEnt, MaxProb, MaxEnt, Semantic Entropy, and VASE), and a white-box method (ReXTrust). Our results show that ReXTrust, a white-box method, achieves the highest AUC across all five models, outperforming the strongest alternative method on each VLM by a statistically significant margin (paired permutation test, p < 0.001 in all cases), reaching a peak AUC of 93.0 on MedGemma-4B. White-box hidden-state access provides a consistent advantage of 19.5 AUC points on average (range: 9.5--33.5), with ReXTrust maintaining strong performance even on LLaVA-v1.6-7B (AUC 79.9), where black-box methods and clustering-based gray-box methods collapse to near-chance performance. Among non-white-box methods, token-level gray-box statistics (MaxEnt, MaxProb) are the strongest alternatives, outperforming both clustering-based gray-box methods (Semantic Entropy, VASE) and black-box approaches on average. We further identify confident confabulation, a failure mode in which models hallucinate with high inter-sample consistency or high token-level probability, as a systemic failure for both consistency and uncertainty-based methods.

cs.CV↗

arXiv2013

A customized flocking algorithm for swarms of sensors tracking a swarm of targets

Wireless mobile sensor networks (WMSNs) are groups of mobile sensing agents with multi-modal sensing capabilities that communicate over wireless networks. WMSNs have more flexibility in terms of deployment and exploration abilities over static sensor networks. Sensor networks have a wide range of applications in security and surveillance systems, environmental monitoring, data gathering for network-centric healthcare systems, monitoring seismic activities and atmospheric events, tracking traffic congestion and air pollution levels, localization of autonomous vehicles in intelligent transportation systems, and detecting failures of sensing, storage, and switching components of smart grids. The above applications require target tracking for processes and events of interest occurring in an environment. Various methods and approaches have been proposed in order to track one or more targets in a pre-defined area. Usually, this turns out to be a complicated job involving higher order mathematics coupled with artificial intelligence due to the dynamic nature of the targets. To optimize the resources we need to have an approach that works in a more straightforward manner while resulting in fairly satisfactory data. In this paper we have discussed the various cases that might arise while flocking a group of sensors to track targets in a given environment. The approach has been developed from scratch although some basic assumptions have been made keeping in mind some previous theories. This paper outlines a customized approach for feasibly tracking swarms of targets in a specific area so as to minimize the resources and optimize tracking efficiency.

cs.OH↗