33 citations · 140 across the 19 of their papers we have counts for
11 papers · 1 filter
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Joint Debiased Representation and Image Clustering Learning with Self-Supervision
Shunjie-Fabian Zheng, JaeEun Nam, Emilio Dorigatti +3
Contrastive learning is among the most successful methods for visual representation learning, and its performance can be further improved by jointly performing clustering on the le…
Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images
Farzin Soleymani, Mohammad Eslami, Tobias Elze +2
We propose a Deep Variational Clustering (DVC) framework for unsupervised representation learning and clustering of large-scale medical images. DVC simultaneously learns the multiv…
Deep Bregman Divergence for Contrastive Learning of Visual Representations
Mina Rezaei, Farzin Soleymani, Bernd Bischl +1
Deep Bregman divergence measures divergence of data points using neural networks which is beyond Euclidean distance and capable of capturing divergence over distributions. In this…
Joint Debiased Representation Learning and Imbalanced Data Clustering
Mina Rezaei, Emilio Dorigatti, David Ruegamer +1
One of the most promising approaches for unsupervised learning is combining deep representation learning and deep clustering. Some recent works propose to simultaneously learn repr…
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data
Mina Rezaei, Haojin Yang, Christoph Meinel
We propose a new generative adversarial architecture to mitigate imbalance data problem for the task of medical image semantic segmentation where the majority of pixels belong to a…
Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation
Mina Rezaei, Haojin Yang, Christoph Meinel
We propose a new generative adversarial architecture to mitigate imbalance data problem in medical image semantic segmentation where the majority of pixels belongs to a healthy reg…
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.…