Adversarial Networks for Camera Pose Regression and Refinement
Despite recent advances on the topic of direct camera pose regression using neural networks, accurately estimating the camera pose of a single RGB image still remains a challenging …
Adaptive image-feature learning for disease classification using inductive graph networks
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 …
hendrik-burwinkel
Fusing unsupervised and supervised deep learning for white matter lesion segmentation
Unsupervised Deep Learning for Medical Image Analysis is increasingly gaining attention, since it relieves from the need for annotating training data. Recently, deep generative …
When regression meets manifold learning for object recognition and pose estimation
In this work, we propose a method for object recognition and pose estimation from depth images using convolutional neural networks. Previous methods addressing this problem rely on …
Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis
MICCAI 2017 is again hosting the Joint MICCAI-Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (MICCAI CVII-STENT), focusing on the …
danail-v.-stoyanov
Domain and geometry agnostic CNNs for left atrium segmentation in 3D ultrasound
Segmentation of the left atrium and deriving its size can help to predict and detect various cardiovascular conditions. Automation of this process in 3D Ultrasound image data is …
markus-a-degel
X-Ray PoseNet: 6 DoF pose estimation for mobile X-Ray devices
Precise reconstruction of 3D volumes from X-ray projections requires precisely pre-calibrated systems where accurate knowledge of the systems geometric parameters is known ahead. …
X-ray in-depth decomposition: Revealing the latent structures
X-ray is the most readily available imaging modality and has a broad range of applications that spans from diagnosis to intra-operative guidance in cardiac, orthopedics, and trauma …
Semi-supervised deep learning for fully convolutional networks
Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to …
Automatic classification of proximal femur fractures based on attention models
We target the automatic classification of fractures from clinical X-Ray images following the Arbeitsgemeinschaft Osteosynthese (AO) classification standard. We decompose the …
