no code implementations • 14 Feb 2023 • Christopher Schmied, Michael Nelson, Sergiy Avilov, Gert-Jan Bakker, Cristina Bertocchi, Johanna Bischof, Ulrike Boehm, Jan Brocher, Mariana Carvalho, Catalin Chiritescu, Jana Christopher, Beth Cimini, Eduardo Conde-Sousa, Michael Ebner, Rupert Ecker, Kevin Eliceiri, Julia Fernandez-Rodriguez, Nathalie Gaudreault, Laurent Gelman, David Grunwald, Tingting Gu, Nadia Halidi, Mathias Hammer, Matthew Hartley, Marie Held, Florian Jug, Varun Kapoor, Ayse Aslihan Koksoy, Judith Lacoste, Sylvia Le Dévédec, Sylvie Le Guyader, Penghuan Liu, Gabriel Martins, Aastha Mathur, Kota Miura, Paula Montero Llopis, Roland Nitschke, Alison North, Adam Parslow, Alex Payne-Dwyer, Laure Plantard, Ali Rizwan, Britta Schroth-Diez, Lucas Schütz, Ryan T. Scott, Arne Seitz, Olaf Selchow, Ved Sharma, Martin Spitaler, Sathya Srinivasan, Caterina Strambio De Castillia, Douglas Taatjes, Christian Tischer, Helena Klara Jambor
Images document scientific discoveries and are prevalent in modern biomedical research.
1 code implementation • 3 Sep 2021 • Amirreza Mahbod, Gerald Schaefer, Rupert Ecker, Isabella Ellinger
Foot ulcer is a common complication of diabetes mellitus and, associated with substantial morbidity and mortality, remains a major risk factor for lower leg amputations.
1 code implementation • MICCAI Workshop COMPAY 2021 • Benjamin Bancher, Amirreza Mahbod, Isabella Ellinger, Rupert Ecker, Georg Dorffner
Recently, instance-aware segmentation methods such as Mask R-CNN have been proposed to enable unified instance detection and segmentation, even in overlapping cases.
1 code implementation • 2 Jan 2021 • Amirreza Mahbod, Gerald Schaefer, Benjamin Bancher, Christine Löw, Georg Dorffner, Rupert Ecker, Isabella Ellinger
Analysis of FS-derived H&E stained images can be more challenging as rapid preparation, staining, and scanning of FS sections may lead to deterioration in image quality.
no code implementations • 15 Nov 2020 • Amirreza Mahbod, Gerald Schaefer, Rupert Ecker, Isabella Ellinger
Our proposed method is shown to yield excellent classification performance, obtaining an accuracy of of 94. 48% and a weighted F1-score of 94. 54% on the ICPR 2020 Pollen Grain Classification Challenge training dataset based on five-fold cross-validation.
1 code implementation • 28 Aug 2020 • Amirreza Mahbod, Philipp Tschandl, Georg Langs, Rupert Ecker, Isabella Ellinger
In this study, we explicitly investigated the impact of using skin lesion segmentation masks on the performance of dermatoscopic image classification.
no code implementations • 25 Jun 2020 • Amirreza Mahbod, Gerald Schaefer, Chunliang Wang, Rupert Ecker, Georg Dorffner, Isabella Ellinger
Our results show that using very small images (of size 64x64 pixels) degrades the classification performance, while images of size 128x128 pixels and above support good performance with larger image sizes leading to slightly improved classification.
no code implementations • 27 Feb 2017 • Amirreza Mahbod, Gerald Schaefer, Chunliang Wang, Rupert Ecker, Isabella Ellinger
In this work, we propose a fully automatic computerised method for skin lesion classification which employs optimised deep features from a number of well-established CNNs and from different abstraction levels.