Md Rakibul Islam

Md Rakibul Islam, Riad Hassan, Abdullah Nazib et al.

Deep learning has achieved remarkable accuracy in medical image segmentation, particularly for larger structures with well-defined boundaries. However, its effectiveness can be challenged by factors such as irregular object shapes and edges, non-smooth surfaces, small target areas, etc. which complicate the ability of networks to grasp the intricate and diverse nature of anatomical regions. In response to these challenges, we propose an Adaptive Focal Loss (A-FL) that takes both object boundary smoothness and size into account, with the goal to improve segmentation performance in intricate anatomical regions. The proposed A-FL dynamically adjusts itself based on an object's surface smoothness, size, and the class balancing parameter based on the ratio of targeted area and background. We evaluated the performance of the A-FL on the PICAI 2022 and BraTS 2018 datasets. In the PICAI 2022 dataset, the A-FL achieved an Intersection over Union (IoU) score of 0.696 and a Dice Similarity Coefficient (DSC) of 0.769, outperforming the regular Focal Loss (FL) by 5.5% and 5.4% respectively. It also surpassed the best baseline by 2.0% and 1.2%. In the BraTS 2018 dataset, A-FL achieved an IoU score of 0.883 and a DSC score of 0.931. Our ablation experiments also show that the proposed A-FL surpasses conventional losses (this includes Dice Loss, Focal Loss, and their hybrid variants) by large margin in IoU, DSC, and other metrics. The code is available at https://github.com/rakibuliuict/AFL-CIBM.git.

Farha Kamal, Md Humaun Kabir, Md Rakibul Islam

GitHub issue trackers contain millions of developer-written quality concerns, including performance bottlenecks and security vulnerabilities, yet no publicly available GitHub dataset classifies these into fine-grained software quality categories. We construct and release GitReq GitHub Requirement Issue, comprising 6,302 expert-validated requirements mined from 55,588 raw GitHub candidates across 4,080 repositories, labeled across eight ISO/IEC 25010:2011-aligned categories: Performance, Security, Portability, Availability, Fault-tolerance, Scalability, Maintainability, and a Functional baseline. Dataset construction involved category-specific triple-signal GitHub mining, separate non-functional requirement (NFR) and functional requirement (FR) preprocessing pipelines with per-category parameters, and expert human annotation achieving substantial inter-annotator agreement (Fleiss' Kappa~=~0.72). Zero-shot evaluation with four large language models (LLMs) establishes baselines, with GPT-5.2 reaching the highest macro-averaged F1 of 0.641. GitReq is publicly released with full materials to advance research in automated requirement classification and software quality analysis.