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Module guide

Modul CS4374-KP06

Medical Deep Learning (MDL)

Duration:


1 Semester
Turnus of offer:


each summer semester
Credit points:


6
Course of studies, specific field and terms:
  • Master MES 2020 (optional subject), computer science / electrical engineering, Arbitrary semester
  • Master Robotics and Autonomous Systems 2019 (optional subject), Elective, 1st or 2nd semester
  • Master Medical Informatics 2014 (optional subject), medical computer science, 1st or 2nd semester
  • Master MES 2014 (optional subject), computer science / electrical engineering, 1st or 2nd semester
  • Master Medical Informatics 2019 (advanced module), medical computer science, 1st or 2nd semester
Classes and lectures:
  • Medical Deep Learning (exercise, 2 SWS)
  • Medical Deep Learning (lecture, 2 SWS)
Workload:
  • 60 Hours in-classroom work
  • 80 Hours private studies
  • 40 Hours exam preparation
Contents of teaching:
  • Cardiac Healthcare:
  • ECG signal analysis for arrhythmia detection or sleep apnea and for mobile low-cost devices
  • MRI sequence analysis for anatomical segmentation and temporal modelling
  • Multimodal Clinical Case Retrieval / Prediction:
  • Pathology and Semantic Image Retrieval and Localisation
  • Analysis of text / natural language (radiology reports/study articles) for multimodal data mining in Electronic Health Records (EHR)
  • Computer Aided Detection and Disease Classification:
  • CT Lung nodule detection for cancer screening with data augmentation and transfer learning
  • Weakly-supervised abnormality detection and biomarker discovery
  • Interpretable and reliable deep learning systems
  • Human interaction and correction within deep learning models
  • Visualisation of uncertainty and internally learned representations
  • Deep Learning Concepts, Architectures and Hardware
  • Convolutional Neural Networks, Layers, Deep Residual Learning
  • Losses, Derivatives, Large-scale Stochastic Optimisation
  • Directed Acyclic Graph Networks, Generative Adversarial Networks
  • Cloud Computing, GPUs, Low Precision Computing, DL Frameworks
Qualification-goals/Competencies:
  • Students know the importance of data security, patient anonymisation and ethics for clinical studies involving sensitive data
  • They know methods and tools to collect, preprocess, store and annotate large datasets for deep learning from medical data
  • They have an in-depth understanding of deep / convolutional neural networks for general data (signals / text / images) processing, their learning process and evaluation of their performance on unseen data
  • They understand the principles of weakly-supervised learning, transfer learning, concept discovery and generative adversarial networks
  • They know how to explore learned feature representations for retrieval and visualisation of high-dimensional abstract data
  • They can implement modern network architectures in DL frameworks and are able to adapt and extend them to given problems in medicine
  • They have a broad overview of current applications of deep learning in medicine in both research and clinical practice and can transfer their knowledge to newly emerging domains
Grading through:
  • Oral examination
Responsible for this module:
Teachers:
Literature:
  • Ian Goodfellow, Yoshua Bengio and Aaron Courville: Deep Learning - The MIT Press
Language:
  • English, except in case of only German-speaking participants
Notes:

Admission requirements for taking the module:
- None

Admission requirements for taking module examination(s):
- Successful completion of exercise assignments and programming tasks as specified at the beginning of the semester.

Module Exam(s):
- CS4374-L1 Medical Deep Learning, , oral examination.

Letzte Änderung:
24.9.2021