ThesesPage

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Diploma, Master and Bachelor Theses

Running Theses

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Deep Learning for Tool Detection and Tracking in Microsurgery (Bachelor Thesis)

The aim of this project is the investigation of the state-of-the-art deep learning architectures and frameworks with the purpose of detection and tracking of instruments in retinal microsurgeries. An implementation of a deep learning based instrument detection workflow shall be provided at the end of the project.
Supervisor:Hasan Sarhan, Dr. Mehmet Yigitsoy
Director:Prof. Nassir Navab
Student:Luca Alessandro Dombetzki
start-end:01.04.2018 -
Localization of disease with limited supervision in chest radiographs (Project)

Chest radiographs are known as the most widely used type of scan in the world. Developing a computer-aided diagnosis can help the radiologists by decreasing the diagnostic error rate and reducing the reading time, which eventually increases the overall performance of the workflow. Recently, a large research efforts have been devoted to developing an automatic diagnosis method with publically available chest radiography datasets. It is important to accurately diagnose and localize the disease at the same time. The performance of deep learning highly depends on the label in the training dataset. However, it is a very time-consuming and expensive task to get an annotation from medical experts for the location of the disease in chest radiographs. In this project, an automatic deep learning method trained with limited supervision for localizing the disease in chest radiographs will be investigated.
Supervisor:Dr. Seong Tae Kim, Ashkan Khakzar
Director:Prof. Dr. Nassir Navab
Student:
start-end: -
Evaluation of real-time dense reconstruction for robotic navigation (Master Thesis)

Supervisor:Nikolas Brasch
Director:Federico Tombari
Student:
start-end: -
Distributed SLAM - Jointly mapping 3D Geometry (DA/MA/BA)

Exploring an unknown scene and self-positioning within it, is a common and well-studied problem in Computer Vision which is known as SLAM (Simultaneous Localization and Mapping). Core fields of application are autonomous cooperative robotics and vehicles as well as tracking and detection systems in the medical domain. Traditional methods target a single system, equipped with image sensors, exploring the scene and building up a map for localization (e.g. a single robot or drone moving within an unknown environment). New approaches also incorporate information from other sensors such as IMUs, gyro or GPS. Another objective for the determination of the position is outside-in-tracking of an object via marker tracking with external sensors, thus providing the relative position of an object with respect to the tracking system. To overcome the line-of-sight problem of outside-in-tracking, and the singularity constraint of traditional SLAM methods, the project aims to develop a distributed SLAM approach. Multiple systems (referred to as sensor nodes hereafter), equipped with an image sensor, contribute to a common map of the scene for localization, while being also tracked by outside-in-tracking for accuracy. Thus, accuracy and applicability can be elevated with a distributed SLAM approach, combining the information of multiple sensor nodes and an external tracking system. Furthermore, the necessity of complicated and error prone calibration processes for individual systems within one application scenario can be avoided. The objective is to develop a generative distributed SLAM approach for challenging scenes and applications. Features like loop detection and closing, pose graph optimization, re-localization and mapping should be extended to a distributed approach, also enabling scalability.
Supervisor:Patrick Ruhkamp, Benjamin Busam
Director:Prof. Dr. Nassir Navab
Student:Joe Bedard
start-end: -
3D Human Pose Estimation from RGB Images (DA/MA/BA)

Supervisor:Nikolas Brasch
Director:Federico Tombari
Student:
start-end: -
Incremental Learning For Robotic Grasping (Master Thesis)

Supervisor:Fabian Manhardt
Director:Federico Tombari
Student:Pengyuan Wang
start-end: -
Rethinking Deep Learning based Monocular Depth Prediction (Master Thesis)

We are looking for a motivated student who wants to work in the topic of monocular depth prediction using deep learning. Predicting depth from a single color image is a challenging and under-constrained task, and as such, active research is happening that incorporates CNNs. Recent works typically do not enforce an understanding for the objects in the scene. In contrast, our goal is to rethink depth prediction and create an object-aware model, which might lead to more accurate depth.
Supervisor:Helisa Dhamo
Director:Federico Tombari
Student:
start-end: -
Inverse Problems in PDE-driven Processes Using Deep Learning (IDP)

We are looking for extremely motivated student to work on the topic "Inverse Problems in PDE-driven Processes Using Deep Learning". The scope of this project is the intersection of numerical methods and machine learning. The objective is to develop theoretical framework and efficient algorithms that can be applied to broad class of PDE-driven systems. However, we can tailor the focus and scope of the project to your preferences.
Supervisor:Suprosanna Shit
Director:Prof. Bjoern Menze
Student:
start-end: -
Learning to learn: Which data we have to annotate first in medical applications? (Master Thesis)

Although the semi-supervised or unsupervised learning has been developed recently, the performance of them is still bound to the performance of fully-supervised learning. However, the cost of the annotation is extremely high in medical applications. It requires medical specialists (radiologists or pathologists) required to annotate the data. For those reasons, it is almost impossible to annotate all available dataset and sometimes, the only a subset of a dataset is possible to be selected for annotation due to the limited budget. Active learning is the research field which tries to deal with this problem [1-5]. Previous studies have been conducted in mainly three approaches: an uncertainty-based approach, a diversity-based approach, and expected model change [3]. These studies have been verified that active learning has the potential to reduce annotation cost. In this project, we aim to propose a novel active learning method which learns a simple uncertainty calculator to select more informative data to learn the current deep neural networks in medical applications.
Supervisor:Dr. Seong Tae Kim
Director:Prof. Dr. Nassir Navab
Student:Farrukh Mushtaq
start-end: -
Learning a new disease with less forgetting strategy (Master Thesis)

Recently, deep learning has great success in various applications such as image recognition, object detection, and medical applications, etc. However, in the real world deployment, the number of tasks continues to grow, or the entire tasks cannot be given at once. In other words, a new type of lesion could be defined by the medical experts and the pre-trained network needs to be re-trained to diagnose these new types of lesions. ‘Class-incremental learning’ is a research area which aims at training the learned model to add new tasks while retaining the knowledge acquired in the past tasks. It is challenging because DNNs are easy to forget previous tasks when learning new tasks (catastrophic forgetting. In real-world scenarios, it is difficult to store all training data which was used when training DNN at the previous time due to the memory and the privacy issues. In this project, we will develop a solution to this problem in medical applications by investigating effective and novel learning method.
Supervisor:Dr. Seong Tae Kim
Director:Prof. Dr. Nassir Navab
Student:
start-end: -
Multiple sclerosis lesion segmentation from Longitudinal brain MRI (IDP)

Longitudinal medical data is defined that imaging data are obtained at more than one time-point where subjects are scanned repeatedly over time. Longitudinal medical image analysis is a very important topic because it can solve some difficulties which are limited when only spatial data is utilized. Temporal information could provide very useful cues for accurately and reliably analyzing medical images. To effectively analyze temporal changes, it is required to segment region-of-interest accurately in a short time. In the series of images acquired over multiple times of imaging, available cues for segmentation become richer with the intermediate predictions. In this project, we will investigate a way to fully exploit this rich source of information.
Supervisor:Dr. Seong Tae Kim, Ashkan Khakzar
Director:Prof. Dr. Nassir Navab
Student:
start-end: -
Understanding Medical Images to Generate Reliable Medical Report (Project)

The reading and interpretation of medical images are usually conducted by specialized medical experts [1]. For example, radiology images are read by radiologists and they write textual reports to describe the findings regarding each area of the body examined in the imaging study. However, writing medical-imaging reports requires experienced medical experts (e.g. experienced radiologists or pathologists) and it is time-consuming [2]. To assist in the administrative duties of writing medical-imaging reports, in recent years, a few research efforts have been devoted to investigating whether it is possible to automatically generate medical image reports for given medical image [3-8]. These methods are usually based on the encoder-decoder architecture which has been widely used for image captioning [9-10]. In this project, a novel automatic medical report generation method is investigated. It is challenging to generate accurate medical reports with large variation due to the high complexity in the natural language [11]. So, the traditional captioning methods suffer a problem where the model duplicates a completely identical sentence of the training set. To address the aforementioned limitations, this project focuses on the development of a reliable medical report generation method.
Supervisor:Dr. Shadi Albarqouni, Dr. Seong Tae Kim
Director:Prof. Dr. Nassir Navab
Student:
start-end: -
Out-of-distribution detection in medical applications (Master Thesis)

Although recent neural networks have achieved great successes when the training and testing data are sampled from the same distribution, in real-world applications, it is unnatural to control the test data distribution. Therefore, it is important for neural networks to be aware of uncertainty when new kinds of inputs (which is called out-of-distribution) are given. In this project, we consider the problem of out-of-distribution detection in neural networks. We will develop a novel out-of-distribution detection based medical diagnosis model to solve the issue of the limited number of training data and unbalance characteristics in real clinical conditions.
Supervisor:Dr. Seong Tae Kim
Director:Prof. Dr. Nassir Navab
Student:
start-end: -
Trajectory Validation using Deep Learning Methods (Master Thesis)

Supervisor:Nikolas Brasch
Director:Federico Tombari
Student:
start-end: -

Finished Theses



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