arXiv subjects

Salma J. Ahmed

Publications and source records attributed to Salma J. Ahmed.

3 recordsLinked to original sources

arXiv2026

J-RAS: Mutual Adaptation for Medical Image Segmentation via Contrastive Retrieval-Augmented Joint Optimization

Manual medical image segmentation by clinicians, though accurate, is time-consuming and variable across experts, while AI-based models automate this process but often falter under limited data and domain shifts. Inspired by how humans learn new tasks through guidance such as how children draw more accurately when shown reference images, we propose Joint Retrieval-Augmented Segmentation (J-RAS), a framework that enables segmentation networks to learn with guidance. J-RAS jointly optimizes a segmentation model and a retrieval model through alternating contrastive and supervised learning, allowing the retrieval network to discover contextually relevant image-mask pairs that refine the segmentation model's anatomical reasoning. Unlike conventional retrieval-based augmentation that passively provides similar samples, J-RAS establishes a mutual adaptation and optimization loop where the retrieval model learns to emphasize segmentation-relevant cues, while the segmentation model leverages retrieved examples to improve boundary delineation, robustness to rare cases and and cross-dataset generalization. Evaluations on two public benchmarks, ACDC and M&Ms, across multiple backbones (U-Net, TransUNet, SAM, and SegFormer) demonstrate the generality and effectiveness of J-RAS. For instance, on ACDC, SegFormer improves from a mean Dice of 0.8708$\pm$0.042 and HD of 1.8130$\pm$2.49 to 0.9115$\pm$0.031 and 1.1489$\pm$0.30. These results highlight how retrieval-guided contrastive optimization bridges human-like guidance and machine-learned precision in medical image segmentation.

cs.CV↗

arXiv2026

Med-SegLens: Latent-Level Model Diffing for Interpretable Medical Image Segmentation

Modern segmentation models achieve strong predictive performance but remain largely opaque, limiting our ability to diagnose failures, understand dataset shift, or intervene in a principled manner. We introduce \textbf{Med-SegLens}, a model-diffing framework that decomposes segmentation model activations into interpretable latent features using sparse autoencoders trained on SegFormer and U-Net. Through cross-architecture and cross-dataset latent alignment across healthy, adult, pediatric, and sub-Saharan African glioma cohorts, we identify a stable backbone of shared representations, while dataset shift is driven by differential reliance on population-specific latents. We show that these latents act as causal bottlenecks for segmentation failures, and that targeted latent-level interventions can correct errors and improve cross-dataset adaption without retraining, recovering performance in 70% of failure cases and improving Dice score from 39.4% to 74.2%. Our results demonstrate that latent-level model diffing provides a practical and mechanistic tool for diagnosing failures and mitigating dataset shift in segmentation models.

cs.CV↗

arXiv2025

Improving Targeted Molecule Generation through Language Model Fine-Tuning Via Reinforcement Learning

Developing new drugs is laborious and costly, demanding extensive time investment. In this paper, we introduce a de-novo drug design strategy, which harnesses the capabilities of language models to devise targeted drugs for specific proteins. Employing a Reinforcement Learning (RL) framework utilizing Proximal Policy Optimization (PPO), we refine the model to acquire a policy for generating drugs tailored to protein targets. The proposed method integrates a composite reward function, combining considerations of drug-target interaction and molecular validity. Following RL fine-tuning, the proposed method demonstrates promising outcomes, yielding notable improvements in molecular validity, interaction efficacy, and critical chemical properties, achieving 65.37 for Quantitative Estimation of Drug-likeness (QED), 321.55 for Molecular Weight (MW), and 4.47 for Octanol-Water Partition Coefficient (logP), respectively. Furthermore, out of the generated drugs, only 0.041% do not exhibit novelty.

q-bio.BM↗