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Learning Algorithms for Medical Big Data Analysis

The big data analytics group is concerned with combining multi-modal imaging and clinical data for improved clinical decision making. Current topics of interest include identification of malignant tumour sub-types in breast cancer, establishing correlations between image-based features, gene expression and disease progression in patients, and developing innovative therapeutic approaches such as immune cell guidance and response activation.  

Colloquium Time Table
Responsible    Person  
10.7.2017  Tobias PertlwieserStudent's Intro Talk: Generative Adversarial Models for Outlier Detection
17.7.2017Dalia RodríguezMappin Decisions Tree to CNN
24.7.2017-No Colloquium due to PRS

Stefan Freitag

Sulaiman Vesal

Student's Final Talk: Improving the Absolute Accuracy of a Robotic C-Arm System with Machine Learning Techniques
Overview: Co-registration of breast lesions in CT and MRI

Jonas Denck

Katrin Mentl

Student's Final Talk: Automatic Billing Code Retrieval from MR-Log Data

Overview: Stroke Detection on NCCT


Markus Schmidt

Rimon Saffoury

Student's Final Talk:

Student's Intro Talk: Automatic Malignancy Estimation for Pulmonary Nodules from CT Images

21.8.2017Sebastian GündelOverview: Chest X-ray Analysis
28.8.2017N/ANo colloquium.
04.9.2017Dalia RodríguezPaper Review: Decision Forests, Convolutional Networks and the Models In-Between
11.9.2017Nora SteinichStudent's Intro Talk: Denoising of low-dose CT images by learning a sparse representation using a convolutional neural network
18.9.2017TBDNo colloquium






Student's Intro Talk: Automatic Deep Learning Lung Lesion Characterization with Combined Application of State-of-the-Art and Image Augmentation Techniques

Slide Sessions

02.10.2017Sulaiman VesalPaper Review: Opens external link in new windowDeep Reinforcement Learning for Active Breast Lesion Detection from DCE-MRI
09.10.2017Tobias PertlwieserStudent's Final Talk: Generative Adversarial Models for Outlier Detection
Student's Final Talk: Implementation and Evaluation of Deep Learning Algorithms for Depth Estimation

Riom Saffoury

Malte Müller

Student's Final Talk: Automatic Malignancy Estimation for Pulmonary Nodules from CT Images

Student's Intro Talk: An Investigation of Parameter Reduction Techniques for Deep Segmentation Networks

30.10.2017Patrick KraussPostponed
06.11.2017Deniz NeufeldStudent's Intro Talk: Generative Networks for Verification of Image Processing Operators in Computed Tomography
13.11.2017No Colloq
Florin Ghesu
Robust Multi-Scale Anatomical Landmark Detection in Incomplete 3D-CT Data

Patrik Krauss

Kai Ehrensperger


Could a neuroscientist understand the brain?
Student's Intro Talk: Deep Learning-based Noise Reduction for Hearing Instrument Applications
11.12.2017Ling Liu
Student's Intro Talk: Deep Learning for Streak Reduction in Computed Tomography

Segmentation Group


*14:15 until 15:15 reserved for a student final talk by segmentation group.

Student's Final Talk: Denoising of low-dose CT images by learning a sparse representation using a convolutional neural network (Bachelor's Thesis)

15.01.2017Philiph GündischStudent's Intro Talk: Anomaly Detection of Data Quantity in Azure Machine Learning Studio (Bachelor Thesis)


Mailing list subscription management page for students and guests.

Running Projects

Breast Tumor Lesions Segmentation

Segmenting and classifying the tumor lesions in breast through Dynamic Contrast-Enhanced MR Images

Medical Image Understanding

Advancing the State-of-the-Art through Integration of Medical Knowledge