arXiv subjects

Emad A. Mohammed

Publications and source records attributed to Emad A. Mohammed.

11 recordsLinked to original sources

arXiv2026

Hybrid Approach for Enhancing Lesion Segmentation in Fundus Images

Choroidal nevi are common benign pigmented lesions in the eye, with a small risk of transforming into melanoma. Early detection is critical to improving survival rates, but misdiagnosis or delayed diagnosis can lead to poor outcomes. Despite advancements in AI-based image analysis, diagnosing choroidal nevi in colour fundus images remains challenging, particularly for clinicians without specialized expertise. Existing datasets often suffer from low resolution and inconsistent labelling, limiting the effectiveness of segmentation models. This paper addresses the challenge of achieving precise segmentation of fundus lesions, a critical step toward developing robust diagnostic tools. While deep learning models like U-Net have demonstrated effectiveness, their accuracy heavily depends on the quality and quantity of annotated data. Previous mathematical/clustering segmentation methods, though accurate, required extensive human input, making them impractical for medical applications. This paper proposes a novel approach that combines mathematical/clustering segmentation models with insights from U-Net, leveraging the strengths of both methods. This hybrid model improves accuracy, reduces the need for large-scale training data, and achieves significant performance gains on high-resolution fundus images. The proposed model achieves a Dice coefficient of 89.7% and an IoU of 80.01% on 1024*1024 fundus images, outperforming the Attention U-Net model, which achieved 51.3% and 34.2%, respectively. It also demonstrated better generalizability on external datasets. This work forms a part of a broader effort to develop a decision support system for choroidal nevus diagnosis, with potential applications in automated lesion annotation to enhance the speed and accuracy of diagnosis and monitoring.

cs.CV↗

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

Opacity-Gradient Driven Density Control for Compact and Efficient Few-Shot 3D Gaussian Splatting

3D Gaussian Splatting (3DGS) struggles in few-shot scenarios, where its standard adaptive density control (ADC) can lead to overfitting and bloated reconstructions. While state-of-the-art methods like FSGS improve quality, they often do so by significantly increasing the primitive count. This paper presents a framework that revises the core 3DGS optimization to prioritize efficiency. We replace the standard positional gradient heuristic with a novel densification trigger that uses the opacity gradient as a lightweight proxy for rendering error. We find this aggressive densification is only effective when paired with a more conservative pruning schedule, which prevents destructive optimization cycles. Combined with a standard depth-correlation loss for geometric guidance, our framework demonstrates a fundamental improvement in efficiency. On the 3-view LLFF dataset, our model is over 40% more compact (32k vs. 57k primitives) than FSGS, and on the Mip-NeRF 360 dataset, it achieves a reduction of approximately 70%. This dramatic gain in compactness is achieved with a modest trade-off in reconstruction metrics, establishing a new state-of-the-art on the quality-vs-efficiency Pareto frontier for few-shot view synthesis.

cs.CV↗

arXiv2025

Prostate Capsule Segmentation from Micro-Ultrasound Images using Adaptive Focal Loss

Micro-ultrasound (micro-US) is a promising imaging technique for cancer detection and computer-assisted visualization. This study investigates prostate capsule segmentation using deep learning techniques from micro-US images, addressing the challenges posed by the ambiguous boundaries of the prostate capsule. Existing methods often struggle in such cases, motivating the development of a tailored approach. This study introduces an adaptive focal loss function that dynamically emphasizes both hard and easy regions, taking into account their respective difficulty levels and annotation variability. The proposed methodology has two primary strategies: integrating a standard focal loss function as a baseline to design an adaptive focal loss function for proper prostate capsule segmentation. The focal loss baseline provides a robust foundation, incorporating class balancing and focusing on examples that are difficult to classify. The adaptive focal loss offers additional flexibility, addressing the fuzzy region of the prostate capsule and annotation variability by dilating the hard regions identified through discrepancies between expert and non-expert annotations. The proposed method dynamically adjusts the segmentation model's weights better to identify the fuzzy regions of the prostate capsule. The proposed adaptive focal loss function demonstrates superior performance, achieving a mean dice coefficient (DSC) of 0.940 and a mean Hausdorff distance (HD) of 1.949 mm in the testing dataset. These results highlight the effectiveness of integrating advanced loss functions and adaptive techniques into deep learning models. This enhances the accuracy of prostate capsule segmentation in micro-US images, offering the potential to improve clinical decision-making in prostate cancer diagnosis and treatment planning.

eess.IV↗

arXiv2025

Taylor-Series Expanded Kolmogorov-Arnold Network for Medical Imaging Classification

Effective and interpretable classification of medical images is a challenge in computer-aided diagnosis, especially in resource-limited clinical settings. This study introduces spline-based Kolmogorov-Arnold Networks (KANs) for accurate medical image classification with limited, diverse datasets. The models include SBTAYLOR-KAN, integrating B-splines with Taylor series; SBRBF-KAN, combining B-splines with Radial Basis Functions; and SBWAVELET-KAN, embedding B-splines in Morlet wavelet transforms. These approaches leverage spline-based function approximation to capture both local and global nonlinearities. The models were evaluated on brain MRI, chest X-rays, tuberculosis X-rays, and skin lesion images without preprocessing, demonstrating the ability to learn directly from raw data. Extensive experiments, including cross-dataset validation and data reduction analysis, showed strong generalization and stability. SBTAYLOR-KAN achieved up to 98.93% accuracy, with a balanced F1-score, maintaining over 86% accuracy using only 30% of the training data across three datasets. Despite class imbalance in the skin cancer dataset, experiments on both imbalanced and balanced versions showed SBTAYLOR-KAN outperforming other models, achieving 68.22% accuracy. Unlike traditional CNNs, which require millions of parameters (e.g., ResNet50 with 24.18M), SBTAYLOR-KAN achieves comparable performance with just 2,872 trainable parameters, making it more suitable for constrained medical environments. Gradient-weighted Class Activation Mapping (Grad-CAM) was used for interpretability, highlighting relevant regions in medical images. This framework provides a lightweight, interpretable, and generalizable solution for medical image classification, addressing the challenges of limited datasets and data-scarce scenarios in clinical AI applications.

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↗

arXiv2021

Deep Learning Approach for Aggressive Driving Behaviour Detection

Driving behaviour is one of the primary causes of road crashes and accidents, and these can be decreased by identifying and minimizing aggressive driving behaviour. This study identifies the timesteps when a driver in different circumstances (rush, mental conflicts, reprisal) begins to drive aggressively. An observer (real or virtual) is needed to examine driving behaviour to discover aggressive driving occasions; we overcome this problem by using a smartphone's GPS sensor to detect locations and classify drivers' driving behaviour every three minutes. To detect timeseries patterns in our dataset, we employ RNN (GRU, LSTM) algorithms to identify patterns during the driving course. The algorithm is independent of road, vehicle, position, or driver characteristics. We conclude that three minutes (or more) of driving (120 seconds of GPS data) is sufficient to identify driver behaviour. The results show high accuracy and a high F1 score.

cs.LG↗

arXiv2021

Dynamic and Systematic Survey of Deep Learning Approaches for Driving Behavior Analysis

Improper driving results in fatalities, damages, increased energy consumptions, and depreciation of the vehicles. Analyzing driving behaviour could lead to optimize and avoid mentioned issues. By identifying the type of driving and mapping them to the consequences of that type of driving, we can get a model to prevent them. In this regard, we try to create a dynamic survey paper to review and present driving behaviour survey data for future researchers in our research. By analyzing 58 articles, we attempt to classify standard methods and provide a framework for future articles to be examined and studied in different dashboards and updated about trends.

cs.LG↗

arXiv2021

Detection of Alzheimer's Disease Using Graph-Regularized Convolutional Neural Network Based on Structural Similarity Learning of Brain Magnetic Resonance Images

Objective: This paper presents an Alzheimer's disease (AD) detection method based on learning structural similarity between Magnetic Resonance Images (MRIs) and representing this similarity as a graph. Methods: We construct the similarity graph using embedded features of the input image (i.e., Non-Demented (ND), Very Mild Demented (VMD), Mild Demented (MD), and Moderated Demented (MDTD)). We experiment and compare different dimension-reduction and clustering algorithms to construct the best similarity graph to capture the similarity between the same class images using the cosine distance as a similarity measure. We utilize the similarity graph to present (sample) the training data to a convolutional neural network (CNN). We use the similarity graph as a regularizer in the loss function of a CNN model to minimize the distance between the input images and their k-nearest neighbours in the similarity graph while minimizing the categorical cross-entropy loss between the training image predictions and the actual image class labels. Results: We conduct extensive experiments with several pre-trained CNN models and compare the results to other recent methods. Conclusion: Our method achieves superior performance on the testing dataset (accuracy = 0.986, area under receiver operating characteristics curve = 0.998, F1 measure = 0.987). Significance: The classification results show an improvement in the prediction accuracy compared to the other methods. We release all the code used in our experiments to encourage reproducible research in this area

cs.CV↗

arXiv2020

A Review of Artificial Intelligence Technologies for Early Prediction of Alzheimer's Disease

Alzheimer's Disease (AD) is a severe brain disorder, destroying memories and brain functions. AD causes chronically, progressively, and irreversibly cognitive declination and brain damages. The reliable and effective evaluation of early dementia has become essential research with medical imaging technologies and computer-aided algorithms. This trend has moved to modern Artificial Intelligence (AI) technologies motivated by deeplearning success in image classification and natural language processing. The purpose of this review is to provide an overview of the latest research involving deep-learning algorithms in evaluating the process of dementia, diagnosing the early stage of AD, and discussing an outlook for this research. This review introduces various applications of modern AI algorithms in AD diagnosis, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Automatic Image Segmentation, Autoencoder, Graph CNN (GCN), Ensemble Learning, and Transfer Learning. The advantages and disadvantages of the proposed methods and their performance are discussed. The conclusion section summarizes the primary contributions and medical imaging preprocessing techniques applied in the reviewed research. Finally, we discuss the limitations and future outlooks.

eess.IV↗