ExaMode and YourTerm collaboration

The ExaMode consortium has officially started a collaboration with the YourTerm – Terminology without borders project of the Terminology Coordination Unit (TermCoord).

The main aim of the project is to enhance communication in a number of domains by tailoring terminology to citizens’ needs through the use of several multilingual efforts.

The collaboration fits in the context of the YourTerm MED project in order to collect the Examode terminology in a new sub-project on Pathology and make it available for different languages and for supporting expert-layperson communication.

ExaMode at the Artificial Ontellingence in Healthcare Workshop

Svetla Boytcheva (Sirma AI – @Ontotext) participated at the stakeholders’ workshop “Artificial Intelligence in Healthcare: paving the way with standardization (27 October 2020 )  #AIStandards4Health organized by #CEN & #CENELEC – European Standardization.

There, she discussed challenges, opportunities, and standardization AI solutions for the healthcare sector, all central topics of the ExaMode project.

Papers accepted for the SPIE conference on Digital Pathology

Two papers from Radboudumc have been accepted for oral presentation at the SPIE conference on Digital Pathology, which will be held in February 2021 in San Diego. The abstracts are available here.

Neural image compression for non-small cell lung cancer subtype classification in H&E stained whole-slide images
Paper 11603-1
Author(s): Witali Aswolinskiy, David Tellez, Gabriel Raya, Lieke van der Woude, Monika Looijen-Salamon, Jeroen van der Laak, Katrien Grünberg, Francesco Ciompi, Radboud Univ. Medical Ctr. (Netherlands)

This paper addresses the problem of lung cancer classification, which is one of the topics of ExaMode, and uses whole-slide image compression, which is a technology that is being developed in the project.

Few-shot weakly supervised detection and retrieval in histopathology whole-slide images
Paper 11603-20
Author(s): Mart van Rijthoven, Maschenka Balkenhol, Radboud Univ. Medical Ctr. (Netherlands); Manfredo Atzori, Haute Ecole Spécialisée de Suisse Occidentale (Switzerland); Peter Bult, Radboud Univ. Medical Ctr. (Netherlands); Jeroen van der Laak, Radboud Univ. Medical Ctr. (Netherlands), Linköping Univ. (Sweden); Francesco Ciompi, Radboud Univ. Medical Ctr. (Netherlands)

The second paper contains work from Deliverable D4.1, and it is in collaboration with HES-SO.

People’s choice award at the MICCAI 2020 LABELS Workshop

Congratulations to Sebastian Otálora, Niccolò Marini, Henning Müller, and Manfredo Atzori who received the “People’s choice award” at the MICCAI 2020 LABELS Workshop (Large-scale Annotation of Biomedical data and Expert Label Synthesis).
The article entitled “The Semi-weakly Supervised Learning for Prostate Cancer Image Classification with Teacher-Student Deep Convolutional Networks” is available at the following link: https://link.springer.com/chapter/10.1007%2F978-3-030-61166-8_21