51 citations · 181 across the 9 of their papers we have counts for
10 papers
Back to Simplicity: How to Train Accurate BNNs from Scratch?
Joseph Bethge, Haojin Yang, Marvin Bornstein +1
Binary Neural Networks (BNNs) show promising progress in reducing computational and memory costs but suffer from substantial accuracy degradation compared to their real-valued coun…
SEE: Towards Semi-Supervised End-to-End Scene Text Recognition
Christian Bartz, Haojin Yang, Christoph Meinel
Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In recent years several new systems that try to solve at least one of the t…
Deep Learning for Medical Image Analysis
Mina Rezaei, Haojin Yang, Christoph Meinel
This report describes my research activities in the Hasso Plattner Institute and summarizes my Ph.D. plan and several novels, end-to-end trainable approaches for analyzing medical…
Conditional Adversarial Network for Semantic Segmentation of Brain Tumor
Mina Rezaei, Konstantin Harmuth, Willi Gierke +4
Automated medical image analysis has a significant value in diagnosis and treatment of lesions. Brain tumors segmentation has a special importance and difficulty due to the differe…
Deep Neural Network with l2-norm Unit for Brain Lesions Detection
Mina Rezaei, Haojin Yang, Christoph Meinel
Automated brain lesions detection is an important and very challenging clinical diagnostic task because the lesions have different sizes, shapes, contrasts, and locations. Deep Lea…
Brain Abnormality Detection by Deep Convolutional Neural Network
Mina Rezaei, Haojin Yang, Christoph Meinel
In this paper, we describe our method for classification of brain magnetic resonance (MR) images into different abnormalities and healthy classes based on the deep neural network.…