arXiv subjects

Anand Choudhary

Publications and source records attributed to Anand Choudhary.

3 recordsLinked to original sources

arXiv2026

ODySSeI: An Open-Source End-to-End Framework for Automated Detection, Segmentation, and Severity Estimation of Lesions in Invasive Coronary Angiography Images

Invasive Coronary Angiography (ICA) is the clinical gold standard for the assessment of coronary artery disease. However, its interpretation remains subjective and prone to intra- and inter-operator variability. In this work, we introduce ODySSeI: an Open-source end-to-end framework for automated Detection, Segmentation, and Severity estimation of lesions in ICA images. ODySSeI integrates deep learning-based lesion detection and lesion segmentation models trained using a novel Pyramidal Augmentation Scheme (PAS) to enhance robustness and real-time performance across diverse patient cohorts (2149 patients from Europe, North America, and Asia). Furthermore, we propose a quantitative coronary angiography-free Lesion Severity Estimation (LSE) technique that directly computes the Minimum Lumen Diameter (MLD) and diameter stenosis from the predicted lesion geometry. Extensive evaluation on both in-distribution and out-of-distribution clinical datasets demonstrates ODySSeI's strong generalizability. Our PAS yields large performance gains in highly complex tasks as compared to relatively simpler ones, notably, a 2.5-fold increase in lesion detection performance versus a 1-3\% increase in lesion segmentation performance over their respective baselines. Our LSE technique achieves high accuracy, with predicted MLD values differing by only $\pm$ 2-3 pixels from the corresponding ground truths. On average, ODySSeI processes a raw ICA image within only a few seconds on a CPU and in a fraction of a second on a GPU and is available as a plug-and-play web interface at swisscardia.epfl.ch. Overall, this work establishes ODySSeI as a comprehensive and open-source framework which supports automated, reproducible, and scalable ICA analysis for real-time clinical decision-making.

cs.LG↗

arXiv2025

GaLLoP: Gradient-based Sparse Learning on Low-Magnitude Parameters

Sparse fine-tuning techniques adapt LLMs to downstream tasks by only tuning a sparse subset of model parameters. However, the effectiveness of sparse adaptation depends on optimally selecting the model parameters to be fine-tuned. In this work, we introduce a novel sparse fine-tuning technique named GaLLoP: Gradient-based Sparse Learning on Low-Magnitude Parameters, which fine-tunes only those model parameters which have the largest gradient magnitudes on downstream tasks and the smallest pre-trained magnitudes, intuitively prioritizing parameters that are highly task-relevant, but minimally disruptive to pre-trained knowledge. Our experimentation with LLaMA3 8B and Gemma 2B as base models shows that GaLLoP consistently improves or matches the in-distribution as well as out-of-distribution performance obtained via the usage of other leading parameter-efficient fine-tuning techniques, including LoRA, DoRA, and SAFT. Our analysis demonstrates that GaLLoP mitigates catastrophic forgetting and memorization of task data, as important pre-trained parameters remain unchanged, and stabilizes performance relative to other fine-tuning techniques, robustly generalizing across most random seeds.

cs.LG↗

arXiv2025

ShaNQar: Simulator of Network Quantique

The nature-inspired field of quantum communication has witnessed exciting developments over the past few years with countries all over the world working hard to scale their experimental quantum networks to larger sizes and increased coverage. Evidently, quantum network simulators are the need of the hour as they provide a framework for tuning hardware parameters, optimizing control protocols, and testing configurations of large and complex quantum networks before their deployment in the real world. In this work, we present ShaNQar (Simulator of Network Quantique): a modular and customizable photonic quantum network simulator. It comprises models of components such as photons, lasers, neutral density filters, sources of entangled photon pairs, communication channels, mirrors, waveplates, beam splitters, single photon detectors, and nodes which incorporate a diverse set of tunable parameters for variability and versatility. It enables adaptive timing control and synchronization with virtually no simulation time resolution limit and features a 'plug and play' design for faster coding and efficient execution. We successfully simulated previous real-life experimental setups for Quantum Key Distribution (QKD) and quantum teleportation, thereby, demonstrating the reliability and accuracy of ShaNQar.

quant-ph↗