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Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning
Abdul Hannan, Zahid Mahmood, Rizwan Qureshi, Hazrat Ali
https://arxiv.org/abs/2507.19199

Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning
Automatic classification of Diabetic Retinopathy (DR) can assist ophthalmologists in devising personalized treatment plans, making it a critical component of clinical practice. However, imbalanced data distribution in the dataset becomes a bottleneck in the generalization of deep learning models trained for DR classification. In this work, we combine global attention block (GAB) and category attention block (CAB) into the deep learning model, thus effectively overcoming the imbalanced data dist…

Smart Cuts: Enhance Active Learning for Vulnerability Detection by Pruning Bad Seeds
Xiang Lan, Tim Menzies, Bowen Xu
https://arxiv.org/abs/2506.20444 http…

Smart Cuts: Enhance Active Learning for Vulnerability Detection by Pruning Bad Seeds
Vulnerability detection is crucial for identifying security weaknesses in software systems. However, the effectiveness of machine learning models in this domain is often hindered by low-quality training datasets, which contain noisy, mislabeled, or imbalanced samples. This paper proposes a novel dataset maps-empowered approach that systematically identifies and mitigates hard-to-learn outliers, referred to as "bad seeds", to improve model training efficiency. Our approach can categorize trainin…

Asymptotic Normality of Infinite Centered Random Forests -Application to Imbalanced Classification
Moria Mayala (LPSM), Erwan Scornet (LPSM), Charles Tillier (LMV), Olivier Wintenberger (LPSM)
https://arxiv.org/abs/2506.08548

Asymptotic Normality of Infinite Centered Random Forests -Application to Imbalanced Classification
Many classification tasks involve imbalanced data, in which a class is largely underrepresented. Several techniques consists in creating a rebalanced dataset on which a classifier is trained. In this paper, we study theoretically such a procedure, when the classifier is a Centered Random Forests (CRF). We establish a Central Limit Theorem (CLT) on the infinite CRF with explicit rates and exact constant. We then prove that the CRF trained on the rebalanced dataset exhibits a bias, which can be r…