Katarzyna Bożek

DISK uses deep learning to impute missing skeleton data in animal behavior studies, improving reliability and enabling more accurate motion analysis.

The paper introduces TeLEV, a method to track EV proteomes in immune cells, revealing EV-driven immunomodulation through STAT5-dependent mechanisms.

This study develops a deep learning model to predict DLBCL recurrence using histological images, achieving an F1 score of 0.83 and identifying predictive nuclear features.

This paper develops an unsupervised deep learning method to quantify C. elegans behavior from video data, capturing coiled movements not accessible via traditional methods.

WormSwin segments C. elegans in videos using a vision transformer, achieving high accuracy and enabling tracking of overlapping worms.

This paper develops a CNN-based method to predict HER2 status in esophageal cancer tissue microarrays with high accuracy, outperforming manual assessment and reducing the need for costly follow-up tests.

This paper introduces a computational method to track entire honey bee colonies using video, achieving high accuracy in position and orientation tracking and revealing collective behaviors.

OrganoidTracker automates cell tracking in organoids using machine learning and manual error correction, achieving high accuracy with minimal manual intervention.