All posts by Michael Reiter

Student Topics

Evaluating different transformer architectures for cell classification in Flow Cytometry Data 

Supervisor: Michael Reiter  Background  Flow Cytometry is a laser-based technique to measure antigen expression levels of blood cells. It is used in research as well as in daily clinical routines for immunophenotyping and for monitoring residual numbers of cancer cells during chemotherapy. One patient’s sample contains approximately 50-300k cells (also called events) with up to … Continue reading Evaluating different transformer architectures for cell classification in Flow Cytometry Data  →

Student Topics

Designing efficient Data Augmentation Strategies for Flow Cytometry Data 

Supervisor: Michael Reiter  Background  Flow Cytometry is a laser-based technique to measure antigen expression levels of blood cells. It is used in research as well as in daily clinical routines for immunophenotyping and for monitoring residual numbers of cancer cells during chemotherapy. One patient’s sample contains approximately 50-300k cells (also called events) with up to … Continue reading Designing efficient Data Augmentation Strategies for Flow Cytometry Data  →

Student Topics

Multi-step classification of flow cytometry cell data with set transformers 

Supervisors: Michael Reiter, Matthias Wödlinger, Florian Kowarsch Background  Flow Cytometry is a laser-based technique to measure antigen expression levels of blood cells. It is used in research as well as in daily clinical routines for immunophenotyping and for monitoring residual numbers of cancer cells during chemotherapy. One patient’s sample contains approximately 50-300k cells (also called … Continue reading Multi-step classification of flow cytometry cell data with set transformers  →

Student Topics

CNN based classification of Flow Cytometry Data

Supervisor: Michael Reiter, Florian Kowarsch Background  Flow Cytometry is a laser-based technique to measure antigen expression levels of blood cells. It is used in research as well as in daily clinical routines for immunophenotyping and for monitoring residual numbers of cancer cells during chemotherapy. One patient’s sample contains approximately 50-300k cells (also called events) with … Continue reading CNN based classification of Flow Cytometry Data →

Student Topics

Verbesserung der CAR-T-Zellen Therapie durch Datenanalyse

Supervisor: Michael Reiter (CVL), Manfred Lehner (CCRI) Praktikum/Bachelorarbeit mit Möglichkeit zur Fortführung im Rahmen einer Diplomarbeit in Zusammenarbeit mit dem Christian Doppler Laborator für CAR-T-Zellen der nächsten Generation Dieses CD Labor an der St. Anna Kinderkrebsforschung arbeitet an molekularen Werkzeugen zur Verbesserung der sogenannten CAR-T-Zell-Therapie, einer Krebstherapie, welche auf der Verabreichung von genetisch veränderten Immunzellen beruht. … Continue reading Verbesserung der CAR-T-Zellen Therapie durch Datenanalyse →

Open Positions (inactive)

PhD Position, Analysis of Flow Cytometry Data, project MyeFLOW

Background Acute myeloid leukemia (AML) is the second most frequent leukemia entity in children and adolescents, and definitely the most aggressive variant. Despite continued progress and refinement of therapeutic approaches, about 35% of pediatric patients with the disease still suffer from relapse. Multiparameter flow-cytometry (FCM) is one of the methodologies most useful to monitor the … Continue reading PhD Position, Analysis of Flow Cytometry Data, project MyeFLOW →

Student Topics

Dermoscopic Image Analysis by Deep Learning

Status: available Supervisor: Michael Reiter, Christopher Pramerdorfer The purpose of this work is to classify sequences of dermatoscopy images as “changing” or “not changing”. The term “change” refers to a depicted mole or skin lesion, and a given sequence should be classified as “changing” if the depicted mole changes in a form that indicates  a tumor, not simply … Continue reading Dermoscopic Image Analysis by Deep Learning →