AIMI @ BVM 2024 in Erlangen

At this year’s BVM workshop, researchers from AIMI presented their latest advancements across a range of medical imaging topics.

Jingna Qiu gave a talk on adaptive region selection for active learning in whole slide image semantic segmentation.
AIMI-affiliated researcher Jonas Ammeling presented his work on automated volume-corrected mitotic index calculation through annotation-free deep learning using immunohistochemistry as a reference standard.
Christof Bertram shared his research on cytologic scoring of equine exercise-induced pulmonary hemorrhage, comparing the performance of human experts and a deep learning-based algorithm.
Jonathan Ganz presented his work on assessment of scanner domain shifts in deep multiple instance learning.
Marc Aubreville introduced a comprehensive multi-domain dataset for mitotic figure detection, and also presented another project on few-shot learning for the classification of confocal laser endomicroscopy.
AIMI-affiliated researcher Luis Carlos Rivera presented a comparative analysis of radiomic features and gene expression profiles in histopathology data using graph neural networks.

If you’d like to learn more about our work, don’t hesitate to check out the related publications or get in touch with us directly.
A heartfelt thank you to this year’s BVM organizers for fostering such insightful discussions — we’re already looking forward to BVM 2025 in Flensburg!