(Pro-) Seminar Machine Learning and Computer Tomography

Hello everyone,

All sessions will be held in E1.1 SR 4.12.

Below is the schedule for this semester.

• Paper Assignment: Please verify that you have been assigned the correct paper and confirm your availability on your designated date.

• Initial Sessions: The first two sessions will follow a flipped classroom format, focusing on discussions and inquiries regarding Computed Tomography, Inverse Problems, and Machine Learning. 
  Kindly prepare by reading the script "Machine Learning for Inverse Problems" available in the Materials section. 
  Note that these sessions will not be traditional lectures; Prof. Schuster will be available to answer questions and facilitate discussions, rather than presenting the topics in a lecture format.

• Presentations: Paper presentations will commence on November 27th and conclude on January 15th.

• Submission Deadlines: Ensure that your slides/notes and handout are submitted to me by your assigned deadline.

• Inquiries: Should you have any questions regarding the paper or your presentation, please feel free to contact me via email.

 

Schedule:

Date	Student	Paper title	Deadline
6.11.24		Flipped Classroom
13.11.24		Flipped Classroom
27.11.24	Davide Damiano	RecDNN: deep neural network for image reconstruction from limited view projection data	20.11.24
04.12.24	Ole Wolf	A complementary l1-TV reconstruction algorithm for limited data CT	27.11.24
11.12.24	Mathivathana Ayyappan	Solving ill-posed inverse problems using iterative deep neural networks	04.12.24
18.11.24	Philipp Henrikus	Computationally Efficient Deep Neural Network for Computed Tomography Image Reconstruction	11.12.24
08.01.25	Kamna Dadwal	LEARN: Learned Experts’ Assessment-based Reconstruction Network for Sparse-data CT	01.01.25
15.01.25	Norman Weinland	Learned Primal-Dual Reconstruction	08.01.25

 

Hi,

I will update you here on which papers are still available.

The following papers can still be selected:

• An Educated Warm Start For Deep Image Prior-Based Micro CT Reconstruction
• Computationally Efficient Cascaded Training for Deep Unrolled Network in CT Imaging
• Superiorization-inspired unrolled SART algorithm with U-Net  generated perturbations for sparse-view and limited-angle CT reconstruction
• Structure-aware diffusion for low-dose CT imaging
• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Conditional Invertible Neural Networks for Medical Imaging
• Computed tomography reconstruction using deep image prior and learned reconstruction methods
• One Sample Diffusion Model in Projection Domain for Low-Dose CT Imaging
• Super-Resolution and Sparse View CT Reconstruction