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The seminar takes place continuously (also during the semester break). In order to arrange a talk, please register through our Oberseminar registration formThis can only be done by the project supervisors.

Location: MI 03.13.010 (Seminar Room)

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PresentationDiscussion
Kick-offs10 min5 min
Finals20 min10 min


Schedule:




Date

Time

Student

Title

Type

Supervisor

08.Nov (Fr) 10:30 Paolo Notaro Radar Pulse Sequence Classification with Deep Learning MA Final Magda Paschali
08.Nov (Fr) 11:00 Stefano Gasperini Deinterleaving and Clustering of RF Signals MA Final Magda Paschali
13.Dez (Fr) 10:30 Claudio Benedetti Deep Learning Based Medical Tool Detection for the Use on a Head-Mounted-Display IDP Final Alexander Winkler


Detailed information about the above presentations:



Date & Time

08.November 2019 -- 10:30

Title

Radar Pulse Sequence Classification with Deep Learning

Student

Paolo Notaro

Type

MA Final

Supervisor

Magda Paschali

Director

Nassir Navab

Abstract

Radar emitter classification is a well-established problem in the domain of Electronic Support Measures (ESM). It has been tackled in the past through the use of rule-based systems, and more recently with the use of data-driven approaches, like machine learning. This work proposes to apply modern Deep Learning solutions for sequence labelling and classification to the ESM domain, aims at investigating which approach produces the best results in terms of prediction accuracy, and focuses on robustness, in relation to possibly missing information and as a protection against adversarial attacks.

Date & Time

08.November 2019 -- 11:00

Title

Deinterleaving and Clustering of RF Signals

Student

Stefano Gasperini

Type

MA Final

Supervisor

Magda Paschali

Director

Nassir Navab

Abstract

This master thesis aims to separate the pulsed signals in real-time with deep learning techniques, and replace traditional methods. We will explore a mixture of spectrogram analysis through image segmentation and deep-learning-based clustering and compare it with traditional clustering techniques like DBSCAN.

Date & Time

13.Dezember 2019 -- 10:30

Title

Deep Learning Based Medical Tool Detection for the Use on a Head-Mounted-Display

Student

Claudio Benedetti

Type

IDP Final

Supervisor

Alexander Winkler

Additional supervisors

Philipp Stefan, Hooman Esfandiari

Director

Nassir Navab

Abstract

Traditionally for medical education and training, the apprenticeship model "See One, Do One, Teach One" guaranteed mastery in the healthcare profession through an adequate exposure to a broad range of cases. Due to several working-hour restrictions, surgery has seen training opportunities in the field continuously decreasing while the complexity of interventions is continuously increasing. This discrepancy has led to the development of simulated training environments from cadavers and synthetic models to computer-based simulators. However, trainees progressing from computer-based simulation to cadaver trainings and ultimately patient treatment face a steep learning curve and need methodologies that facilitate this transition.

There is a body of work targeting the use of Virtual and Augmented Reality (VR/AR) for training of healthcare professionals. Most of them are however aimed at the physicians who perform arguably the most critical part of the procedure, are however strongly dependent on the other staff in the operating room as well: Scrub nurses, also called perioperative nurses who support the surgeon while also maintaining patient safety.

Scrub nurses are a major role in the success of surgery as they conduct a large number of tasks, including ensuring the operating room is ready to be set up, preparing the instruments and equipment needed for the surgery, selecting and passing instruments to the surgeon, counting all instruments, sponges and other tools and transporting the patient to the recovery area. Because scrub nurses are so vital to surgical procedures, they may work long hours. They must also have excellent communication skills, because one of their primary duties is working with the surgeon and assisting her with anything she needs during the operation.

A large part of the learning critical to being an experienced scrub nurse is knowledge about the specific workflow of specific procedures, the tools for a specific procedure and when which tools are needed.

AR Head-Mounted-Displays (HMDs) are semitransparent displays devices, worn on the head in front of the users' eyes superimposing virtual objects on the real world (optical see through). HMDs for AR applications have been around since the 1960s. With bulkiness and a high price tag, their use was however limited to laboratory settings for the longest part of their existence. With the advent of the current generation of HMDs that target the consumer and gaming market, their price has considerably dropped and usability and overall quality has improved.

The goal of this interdisciplinary project, is to create an HMD application assisting a scrub nurse. As a first step such a system could be used for training of the nurse, but could ultimately also be transferred into clinical use. For the tasks of the nurse handing specific tools to the surgeon in specific steps in the workflow in the operating room, the main goal of this IDP is the development and integration of an object detection feature into an off the shelf HMD, so that the nurse can pass the correct tool to the surgeon. The special technical challenge in this instance of the problem is a general challenge of AR: Computational speed on a computationally limited platform. There is an abundance of object detection algorithms based on Deep Learning available, however they usually rely on powerful compute hardware, which is expensive, large and has a high power consumption which all are features which prohibit the use in an HMD. On the other hand light weight DL techniques exhibit detection rates, too low for use in healthcare. In this project we propose a hybrid approach, to use the HMD as an in- and output device, however stream data in real time to a powerful workstation, that can perform the detection task.

There are currently several medical training, planning, guidance and assistance systems being developed at Prof. Dr. Navab's NARVIS lab. As a proof of concept one of them should be integrated in the HMD application to evaluate and demonstrate its benefits. In this lab the student is also provided access to HMDs as well as a workstation for the machine learning tasks.



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