Publication

Fast and Robust Segmentation of the Striatum Using Deep Convolutional Neural Networks

Related publications (85)

About
Privacy
Disclaimer

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Fast and Robust Segmentation of the Striatum Using Deep Convolutional Neural Networks

Graph Chatbot

Chat with Graph Search

Large-Scale Image Segmentation with Convolutional Networks

Large-Scale Image Segmentation with Convolutional Networks

Convolutional Neural Networks for Inverse Problems in Imaging—A Review

Crowding asymmetries explained by a model of image segmentation

Keyword-based Image Color Re-rendering with Semantic Segmentation

Towards Scalable Segmentation of 3D Image Stacks

Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model

Learning Complex Sequential Tasks from Demonstration: A Pizza Dough Rolling Case Study

Learning Complex Sequential Tasks from Demonstration: A Pizza Dough Rolling Case Study

From Image-level to Pixel-level Labeling with Convolutional Networks

Towards Scalable Segmentation of 3D Image Stacks

Large-Scale Image Segmentation with Convolutional Networks

Large-Scale Image Segmentation with Convolutional Networks

Learning Complex Sequential Tasks from Demonstration: A Pizza Dough Rolling Case Study

Keyword-based Image Color Re-rendering with Semantic Segmentation

Food/Non-food Image Classification and Food Categorization using Pre-Trained GoogLeNet Model

Learning Complex Sequential Tasks from Demonstration: A Pizza Dough Rolling Case Study

From Image-level to Pixel-level Labeling with Convolutional Networks

Convolutional Neural Networks for Inverse Problems in Imaging—A Review

Crowding asymmetries explained by a model of image segmentation