Generative Enhancement of 3D Image Classifiers

3D Medical Image Synthesis using Generative Adversarial Networks

In this work we propose an architecture for 3D medical image synthesis based on Generative Adversarial Networks. ACM Reference format: Irina Sánchez and Verónica Vilaplana. 2017. 3D Medical Image Synthesis using Generative Adversarial Networks. In Proceedings of womENcourage 2017, Barcelona, Spain, September 2017, 1 pages.

Aspects of Generative and Discriminative Classifiers

Exploiting Generative Models in Discriminative Classifiers

„ Generative probability models deal with missing information and variable length sequences.

Selection of Image Classifiers 1 Selection of Image Classifiers

An approach to classifier combination based on the concept of “dynamic classifier selection” is presented in this letter. The results are reported and they show that the proposed approach allows to develop effective image classification systems.

Exploiting generative models discriminative classifiers • In

Generative probability models such as hidden ~larkov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand , discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often result in classification performance superior to that of the model based approaches. An ideal class...