446 citations · 2.3k across the 135 of their papers we have counts for
23 papers · 1 filter
Real-Time 3D Model Tracking in Color and Depth on a Single CPU Core
Wadim Kehl, Federico Tombari, Slobodan Ilic +1
We present a novel method to track 3D models in color and depth data. To this end, we introduce approximations that accelerate the state-of-the-art in region-based tracking by an o…
Few-Shot Meta-Denoising
Leslie Casas, Attila Klimmek, Gustavo Carneiro +2
We study the problem of few-shot learning-based denoising where the training set contains just a handful of clean and noisy samples. A solution to mitigate the small training set i…
`Project & Excite' Modules for Segmentation of Volumetric Medical Scans
Anne-Marie Rickmann, Abhijit Guha Roy, Ignacio Sarasua +2
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging. Recently, squeeze and excitation (SE) modules and varia…
Perceptual Embedding Consistency for Seamless Reconstruction of Tilewise Style Transfer
Amal Lahiani, Nassir Navab, Shadi Albarqouni +1
Style transfer is a field with growing interest and use cases in deep learning. Recent work has shown Generative Adversarial Networks(GANs) can be used to create realistic images o…
Image to Images Translation for Multi-Task Organ Segmentation and Bone Suppression in Chest X-Ray Radiography
Mohammad Eslami, Solale Tabarestani, Shadi Albarqouni +3
Chest X-ray radiography is one of the earliest medical imaging technologies and remains one of the most widely-used for diagnosis, screening, and treatment follow up of diseases re…
BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl +2
Access to sufficient annotated data is a common challenge in training deep neural networks on medical images. As annotating data is expensive and time-consuming, it is difficult fo…
Multi-modal Graph Fusion for Inductive Disease Classification in Incomplete Datasets
Gerome Vivar, Hendrik Burwinkel, Anees Kazi +3
Clinical diagnostic decision making and population-based studies often rely on multi-modal data which is noisy and incomplete. Recently, several works proposed geometric deep learn…
Learning Interpretable Features via Adversarially Robust Optimization
Ashkan Khakzar, Shadi Albarqouni, Nassir Navab
Neural networks are proven to be remarkably successful for classification and diagnosis in medical applications. However, the ambiguity in the decision-making process and the inter…
Adaptive Image-Feature Learning for Disease Classification Using Inductive Graph Networks
Hendrik Burwinkel, Anees Kazi, Gerome Vivar +4
Recently, Geometric Deep Learning (GDL) has been introduced as a novel and versatile framework for computer-aided disease classification. GDL uses patient meta-information such as…
Fully Automatic Segmentation of 3D Brain Ultrasound: Learning from Coarse Annotations
Julia Rackerseder, Rüdiger Göbl, Nassir Navab +1
Intra-operative ultrasound is an increasingly important imaging modality in neurosurgery. However, manual interaction with imaging data during the procedures, for example to select…
Learning Interpretable Disentangled Representations using Adversarial VAEs
Mhd Hasan Sarhan, Abouzar Eslami, Nassir Navab +1
Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice. It has been recently shown that learning…
Collaboration Analysis Using Deep Learning
Zhang Guo, Kevin Yu, Rebecca Pearlman +2
The analysis of the collaborative learning process is one of the growing fields of education research, which has many different analytic solutions. In this paper, we provided a new…
Weakly-Supervised White and Grey Matter Segmentation in 3D Brain Ultrasound
Beatrice Demiray, Julia Rackerseder, Stevica Bozhinoski +1
Although the segmentation of brain structures in ultrasound helps initialize image based registration, assist brain shift compensation, and provides interventional decision support…
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…
Multi-scale Microaneurysms Segmentation Using Embedding Triplet Loss
Mhd Hasan Sarhan, Shadi Albarqouni, Mehmet Yigitsoy +2
Deep learning techniques are recently being used in fundus image analysis and diabetic retinopathy detection. Microaneurysms are an important indicator of diabetic retinopathy prog…
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…
Attention-based Lane Change Prediction
Oliver Scheel, Naveen Shankar Nagaraja, Loren Schwarz +2
Lane change prediction of surrounding vehicles is a key building block of path planning. The focus has been on increasing the accuracy of prediction by posing it purely as a functi…
LumiPath -- Towards Real-time Physically-based Rendering on Embedded Devices
Laura Fink, Sing Chun Lee, Jie Ying Wu +6
With the increasing computational power of today's workstations, real-time physically-based rendering is within reach, rapidly gaining attention across a variety of domains. These…
Semi-Supervised Few-Shot Learning with Prototypical Random Walks
Ahmed Ayyad, Yuchen Li, Nassir Navab +2
Recent progress has shown that few-shot learning can be improved with access to unlabelled data, known as semi-supervised few-shot learning(SS-FSL). We introduce an SS-FSL approach…
'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images
Abhijit Guha Roy, Shayan Siddiqui, Sebastian Pölsterl +2
Deep neural networks enable highly accurate image segmentation, but require large amounts of manually annotated data for supervised training. Few-shot learning aims to address this…
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…
Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits
Tolga Birdal, Benjamin Busam, Nassir Navab +2
We present a novel and effective method for detecting 3D primitives in cluttered, unorganized point clouds, without axillary segmentation or type specification. We consider the qua…