3 papers
cs.CV2024
TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models
Lisa Weijler, Muhammad Jehanzeb Mirza, Leon Sick +2
Test-Time Training (TTT) proposes to adapt a pre-trained network to changing data distributions on-the-fly. In this work, we propose the first TTT method for 3D semantic segmentati…
eess.IV2023
FATE: Feature-Agnostic Transformer-based Encoder for learning generalized embedding spaces in flow cytometry data
Lisa Weijler, Florian Kowarsch, Michael Reiter +3
While model architectures and training strategies have become more generic and flexible with respect to different data modalities over the past years, a persistent limitation lies…
q-bio.QM2023
Explainable Techniques for Analyzing Flow Cytometry Cell Transformers
Florian Kowarsch, Lisa Weijler, FLorian Kleber +4
Explainability for Deep Learning Models is especially important for clinical applications, where decisions of automated systems have far-reaching consequences. While various post-h…