BioMedAI TWINNING

Future of explainable artificial intelligence research in healthcare

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Winter School XAI 2025: Registration Now Open!

Winter School XAI 2025: Registration Now Open!

At the beginning of the new year, the Winter School XAI 2025 will occur from January 6 to 10, 2025, at the Masaryk Oncology Institute in Brno and the Medical University of Graz. This unique program will provide practical insights into the latest artificial intelligence (AI) advancements in digital pathology.

Participants will engage in training sessions led by experts, covering essential topics such as AI-assisted diagnostics, the significance of explainability in medical AI, and certification processes according to the In Vitro Diagnostic Regulation (IVDR). The program also includes a practical component, allowing participants to work directly with digital pathology data, including whole-slide images and clinical data.

BioMedAI Team Showcases Research at the first ASDP Congress in Seoul

BioMedAI Team Showcases Research at the first ASDP Congress in Seoul

Representatives of the BIoMedAI project participated in the 1st Annual Congress of the Asian Society of Digital Pathology (ASDP), which took place from October 2 to 4, 2024, in Seoul, South Korea.

The delegation included Vít Musil, the team leader from the RationAI lab and
Assistant professor at the Department of Computer Science, along with PhD student Adam Bajger and master's student Adam Kukučka, who presented the results of his research. This research, initially part of his bachelor’s thesis, is now under review.

Czech Open Source Policy Forum Highlights Contributions from BioMedAI

Czech Open Source Policy Forum Highlights Contributions from BioMedAI

On April 24, 2024, the Czech Open Source Policy Forum took place in Brno, featuring prominent discussions on the sustainability of open-source projects within the public sector and academia. Among the attendees was Petr Holub, a representative from the BioMedAI project. He actively participated in discussions focused on the importance of community engagement for the sustainability of these initiatives. Holub's insights contributed to a deeper understanding of how collaborative efforts can enhance the longevity and impact of open-source projects.

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Publications

Provenance of specimen and data – A prerequisite for AI development in computational pathology

December 2023

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Shedding light on the black box of a neural network used to detect prostate cancer in whole slide images by occlusion-based explainability

December 2023

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Privacy Risks of Whole-Slide Image Sharing in Digital Pathology 

May 2023

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AI for life: Trends in artificial intelligence for biotechnology

May 2023

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“The increasing demand for sophisticated clinical diagnostics leads to the need for semi-automatic systems using AI and machine learning to speed up the diagnosis process, and the BioMedAI project aims to establish close cooperation between computer science and clinical experts to develop explainable and trustworthy AI solutions through virtual training, workshops, and summer schools, while also increasing the visibility and presence of explainable AI research in healthcare.”

Petr Holub, Project Lead

Publications

Provenance of specimen and data – A prerequisite for AI development in computational pathology

December 2023

Get more info

Shedding light on the black box of a neural network used to detect prostate cancer in whole slide images by occlusion-based explainability

December 2023

Get more info

Privacy Risks of Whole-Slide Image Sharing in Digital Pathology 

May 2023

Get more info

AI for life: Trends in artificial intelligence for biotechnology

May 2023

Get more info

“The increasing demand for sophisticated clinical diagnostics leads to the need for semi-automatic systems using AI and machine learning to speed up the diagnosis process, and the BioMedAI project aims to establish close cooperation between computer science and clinical experts to develop explainable and trustworthy AI solutions through virtual training, workshops, and summer schools, while also increasing the visibility and presence of explainable AI research in healthcare.”

Petr Holub, Project Lead

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