Dr. Markus Hagenbuchner

Dr. Markus Hagenbuchner

University of Wollongong

H-index: 24

Oceania-Australia

About Dr. Markus Hagenbuchner

Dr. Markus Hagenbuchner, With an exceptional h-index of 24 and a recent h-index of 14 (since 2020), a distinguished researcher at University of Wollongong, specializes in the field of Machine Learning, Modelling of Data Structures, Document classification, Pattern Recognition in videos.

His recent articles reflect a diverse array of research interests and contributions to the field:

On the effects of recursive convolutional layers in convolutional neural networks

Accurate and fast deep learning dose prediction for a preclinical microbeam radiation therapy study using low-statistics Monte Carlo simulations

Mitigating the Adverse Effects of Long-Tailed Data on Deep Learning Models

Exploring the Role of Recursive Convolutional Layer in Generative Adversarial Networks

Going Deeper with Recursive Convolutional Layers

Embedding the Self-Organisation of Deep Feature Maps in the Hamburger Framework can Yield Better and Interpretable Results

Higher order polynomial transformer for fine-grained freezing of gait detection

Sign language translation with hierarchical spatio-temporal graph neural network

Dr. Markus Hagenbuchner Information

University

Position

Associate Professor

Citations(all)

9574

Citations(since 2020)

7900

Cited By

3936

hIndex(all)

24

hIndex(since 2020)

14

i10Index(all)

58

i10Index(since 2020)

21

Email

University Profile Page

University of Wollongong

Google Scholar

View Google Scholar Profile

Dr. Markus Hagenbuchner Skills & Research Interests

Machine Learning

Modelling of Data Structures

Document classification

Pattern Recognition in videos

Top articles of Dr. Markus Hagenbuchner

Title

Journal

Author(s)

Publication Date

On the effects of recursive convolutional layers in convolutional neural networks

Neurocomputing

Johan Chagnon

Markus Hagenbuchner

Ah Chung Tsoi

Franco Scarselli

2024/4/26

Accurate and fast deep learning dose prediction for a preclinical microbeam radiation therapy study using low-statistics Monte Carlo simulations

Cancers

Florian Mentzel

Jason Paino

Micah Barnes

Matthew Cameron

Stéphanie Corde

...

2023/4/4

Mitigating the Adverse Effects of Long-Tailed Data on Deep Learning Models

Din Muhammad Sangrasi

Lei Wang

Markus Hagenbuchner

Peng Wang

2023/12/5

Exploring the Role of Recursive Convolutional Layer in Generative Adversarial Networks

Barbara Toniella Corradini

Paolo Andreini

Markus Hagenbuchner

Franco Scarselli

Ah Chung Tsoi

2023/9/22

Going Deeper with Recursive Convolutional Layers

Johan Chagnon

Markus Hagenbuchner

Ah Chung Tsoi

Franco Scarselli

2023/6/18

Embedding the Self-Organisation of Deep Feature Maps in the Hamburger Framework can Yield Better and Interpretable Results

Jack Humphreys

Markus Hagenbuchner

Zhiyong Wang

Ah Chung Tsoi

2023/6/18

Higher order polynomial transformer for fine-grained freezing of gait detection

IEEE Transactions on Neural Networks and Learning Systems

Renfei Sun

Kun Hu

Kaylena A Ehgoetz Martens

Markus Hagenbuchner

Ah Chung Tsoi

...

2023/4/12

Sign language translation with hierarchical spatio-temporal graph neural network

Jichao Kan

Kun Hu

Markus Hagenbuchner

Ah Chung Tsoi

Mohammed Bennamoun

...

2022

Small beams, fast predictions: a comparison of machine learning dose prediction models for proton minibeam therapy

Medical physics

F Mentzel

K Kröninger

M Lerch

O Nackenhorst

A Rosenfeld

...

2022/12

A step towards treatment planning for microbeam radiation therapy: fast peak and valley dose predictions with 3D U-Nets

arXiv preprint arXiv:2211.11193

Florian Mentzel

Micah Barnes

Kevin Kröninger

Michael Lerch

Olaf Nackenhorst

...

2022/11/21

PO-1558 Fast dose predictions with generative adversarial networks for treatment planning of novel therapies

Radiotherapy and Oncology

F Mentzel

O Nackenhorst

J Weingarten

K Kröninger

A Rosenfeld

...

2022/5/1

Fast and accurate dose predictions for novel radiotherapy treatments in heterogeneous phantoms using conditional 3D‐UNet generative adversarial networks

Medical Physics

Florian Mentzel

Kevin Kröninger

Michael Lerch

Olaf Nackenhorst

Jason Paino

...

2022/5

A Study on the effects of recursive convolutional layers in convolutional neural networks

Neurocomputing

Alberto Rossi

Markus Hagenbuchner

Franco Scarselli

Ah Chung Tsoi

2021/10/14

Graph fusion network-based multimodal learning for freezing of gait detection

IEEE Transactions on Neural Networks and Learning Systems

Kun Hu

Zhiyong Wang

Kaylena A Ehgoetz Martens

Markus Hagenbuchner

Mohammed Bennamoun

...

2021/8/31

Topic-guided local-global graph neural network for image captioning

Jichao Kan

Kun Hu

Zhiyong Wang

Qiuxia Wu

Markus Hagenbuchner

...

2021/7/5

The black box problem of AI in oncology

Journal of Physics: Conference Series

Markus Hagenbuchner

2020/10/1

See List of Professors in Dr. Markus Hagenbuchner University(University of Wollongong)

Co-Authors

H-index: 99
Horst Bunke

Horst Bunke

Universität Bern

H-index: 91
Stewart Trost

Stewart Trost

Queensland University of Technology

H-index: 53
Dylan Cliff

Dylan Cliff

University of Wollongong

H-index: 53
Marco Gori

Marco Gori

Università degli Studi di Siena

H-index: 45
Alessandro Sperduti

Alessandro Sperduti

Università degli Studi di Padova

H-index: 45
Friedhelm Schwenker

Friedhelm Schwenker

Universität Ulm

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