5 citations · 5 across the 5 of their papers we have counts for
5 papers
Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors
Md Abdul Kadir, GowthamKrishna Addluri, Daniel Sonntag
Explainable Artificial Intelligence (XAI) strategies play a crucial part in increasing the understanding and trustworthiness of neural networks. Nonetheless, these techniques could…
Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project
Md Abdul Kadir, Hasan Md Tusfiqur Alam, Pascale Maul +3
Image annotation is one of the most essential tasks for guaranteeing proper treatment for patients and tracking progress over the course of therapy in the field of medical imaging…
EdgeAL: An Edge Estimation Based Active Learning Approach for OCT Segmentation
Md Abdul Kadir, Hasan Md Tusfiqur Alam, Daniel Sonntag
Active learning algorithms have become increasingly popular for training models with limited data. However, selecting data for annotation remains a challenging problem due to the l…
Harmonizing Feature Attributions Across Deep Learning Architectures: Enhancing Interpretability and Consistency
Md Abdul Kadir, Gowtham Krishna Addluri, Daniel Sonntag
Ensuring the trustworthiness and interpretability of machine learning models is critical to their deployment in real-world applications. Feature attribution methods have gained sig…
Fine-tuning of explainable CNNs for skin lesion classification based on dermatologists' feedback towards increasing trust
Md Abdul Kadir, Fabrizio Nunnari, Daniel Sonntag
In this paper, we propose a CNN fine-tuning method which enables users to give simultaneous feedback on two outputs: the classification itself and the visual explanation for the cl…