26 citations · 151 across the 35 of their papers we have counts for
35 papers
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…
Towards Adaptable and Interactive Image Captioning with Data Augmentation and Episodic Memory
Aliki Anagnostopoulou, Mareike Hartmann, Daniel Sonntag
Interactive machine learning (IML) is a beneficial learning paradigm in cases of limited data availability, as human feedback is incrementally integrated into the training process.…
Putting Humans in the Image Captioning Loop
Aliki Anagnostopoulou, Mareike Hartmann, Daniel Sonntag
Image Captioning (IC) models can highly benefit from human feedback in the training process, especially in cases where data is limited. We present work-in-progress on adapting an I…
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching
Duy M. H. Nguyen, Hoang Nguyen, Nghiem T. Diep +9
Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data. While pre-trained dee…
A Virtual Reality Tool for Representing, Visualizing and Updating Deep Learning Models
Hannes Kath, Bengt Lüers, Thiago S. Gouvêa +1
Deep learning is ubiquitous, but its lack of transparency limits its impact on several potential application areas. We demonstrate a virtual reality tool for automating the process…