most citedDetection-aided liver lesion segmentation using deep learning

35 citations · 55 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV2017

ComboGAN: Unrestrained Scalability for Image Domain Translation

Asha Anoosheh, Eirikur Agustsson, Radu Timofte +1

This year alone has seen unprecedented leaps in the area of learning-based image translation, namely CycleGAN, by Zhu et al. But experiments so far have been tailored to merely two…

cs.CV2017

Object Referring in Visual Scene with Spoken Language

Arun Balajee Vasudevan, Dengxin Dai, Luc Van Gool

Object referring has important applications, especially for human-machine interaction. While having received great attention, the task is mainly attacked with written language (tex…

cs.CV201735 cited

Detection-aided liver lesion segmentation using deep learning

Miriam Bellver, Kevis-Kokitsi Maninis, Jordi Pont-Tuset +3

A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and assess the response to the…

cs.LG201710 cited

Optimal transport maps for distribution preserving operations on latent spaces of Generative Models

Eirikur Agustsson, Alexander Sage, Radu Timofte +1

Generative models such as Variational Auto Encoders (VAEs) and Generative Adversarial Networks (GANs) are typically trained for a fixed prior distribution in the latent space, such…

cs.CV2017

Automatic Tool Landmark Detection for Stereo Vision in Robot-Assisted Retinal Surgery

Thomas Probst, Kevis-Kokitsi Maninis, Ajad Chhatkuli +3

Computer vision and robotics are being increasingly applied in medical interventions. Especially in interventions where extreme precision is required they could make a difference.…

cs.CV201710 cited

The WILDTRACK Multi-Camera Person Dataset

Tatjana Chavdarova, Pierre Baqué, Stéphane Bouquet +6

People detection methods are highly sensitive to the perpetual occlusions among the targets. As multi-camera set-ups become more frequently encountered, joint exploitation of the a…