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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 a malignant tumor 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.  

Dates & Rooms:
Monday, 14:00 - 16:00; Room: 00.152-113

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

Colloquium Time Table


Opens internal link in current windowPrior Sessions


Responsible    Person  
14.01.2019Akriti SharmaMaster's thesis final talk: Finding de novo motifs in DNA sequences by deep learning
04.02.2019Daniel ZimmermannMaster's thesis final talk: Multi-task Learning for Segmentation and Classification of Lesions in Mammography Images
25.02.2019Jan Lukas

Journal Club: Clustering to forecast sparse time-series data ECG Signals Using Deep Neural Networks

11.03.2019Oliver Haas

Journal Club: Detecting Potential Adverse Drug Reactions Using a Deep NeuralNetwork Model (Room 00.151-113)

06.05.2019Sulaiman VesalJournal Club: Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation

Karthik Shetty

Kamal Gopikrishnan Nambiar

MT- Final Talk: Image-based heart phase estimation in coronary angiography

MT- Intro Talk: Application of Neuroscience Analysis Techniques in Deep Reinforcement Learning Networks

20.05.2019Chang LiuMT- Intro Talk: Automatic Breast Segmentation for Individualized Diagnostics
03.06.2019Dalia RodriguezJournal club: The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks (ICLR 2019)
24.06.2019No ColloqDue to Dominik's PhD defense 

Nidhi Joshi

Sarkar Johar

Adarsh Bhandary

MT- Intro Talk: Automatic Sleep Stage Scoring from Multi-channel EEG data using Deep Learning

MT- Intro Talk: Abnormality detection on musculoskeletal radiographs

MT- Final Talk: Classification of Lung Nodules in CT Images using Deep Learning

08.07.2019Felix DenzingerJournal Club: Diagnostic accuracy of 3D deep-learning-based fully automated estimation of patient-level minimum fractional flow reserve from coronary computed tomography angiography [EHJ 2019]
15.07.2019Chang LiuMaster's thesis final talk: Automatic breast segmentation for individualized diagnostics
22.07.2019Syben's studentMT Intro Talk: Detecting the yet undetected: A deep learning based approach to classify radial bone in systemic inflammatory diseases measured by HR-pQCT imaging

Ahmed Jalil

Faezeh Nejati Hatamian

MT Final Talk: Deep Learning-based System for Classification of Chronic Obstructive Pulmonary Disease in Computer Tomography Images

MT Intro Talk: Atrial Fibrillation Classification from Short Single-Lead ECG Signals Using Deep Neural Networks

09.09.2019Sha-am Al ShayahMaster's thesis intro talk: Evaluation of the Re-identification Capabilities of Neural Networks with Chest X-ray Data


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