232 citations · 573 across the 29 of their papers we have counts for
6 papers · 1 filter
Radar Emitter Classification with Attribute-specific Recurrent Neural Networks
Paolo Notaro, Magdalini Paschali, Carsten Hopke +2
Radar pulse streams exhibit increasingly complex temporal patterns and can no longer rely on a purely value-based analysis of the pulse attributes for the purpose of emitter classi…
Signal Clustering with Class-independent Segmentation
Stefano Gasperini, Magdalini Paschali, Carsten Hopke +2
Radar signals have been dramatically increasing in complexity, limiting the source separation ability of traditional approaches. In this paper we propose a Deep Learning-based clus…
Deep Learning Under the Microscope: Improving the Interpretability of Medical Imaging Neural Networks
Magdalini Paschali, Muhammad Ferjad Naeem, Walter Simson +3
In this paper, we propose a novel interpretation method tailored to histological Whole Slide Image (WSI) processing. A Deep Neural Network (DNN), inspired by Bag-of-Features models…
End-to-End Learning-Based Ultrasound Reconstruction
Walter Simson, Rüdiger Göbl, Magdalini Paschali +4
Ultrasound imaging is caught between the quest for the highest image quality, and the necessity for clinical usability. Our contribution is two-fold: First, we propose a novel full…
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation
Magdalini Paschali, Stefano Gasperini, Abhijit Guha Roy +2
Model architectures have been dramatically increasing in size, improving performance at the cost of resource requirements. In this paper we propose 3DQ, a ternary quantization meth…
Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness
Magdalini Paschali, Walter Simson, Abhijit Guha Roy +4
In this paper we propose a novel augmentation technique that improves not only the performance of deep neural networks on clean test data, but also significantly increases their ro…