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Found 4 papers, 2 papers with code
TTT-KD: Test-Time Training for 3D Semantic Segmentation through Knowledge Distillation from Foundation Models
Given access to paired image-pointcloud (2D-3D) data, we first optimize a 3D segmentation backbone for the main task of semantic segmentation using the pointclouds and the task of 2D $\to$ 3D KD by using an off-the-shelf 2D pre-trained foundation model.
FATE: Feature-Agnostic Transformer-based Encoder for learning generalized embedding spaces in flow cytometry data
This limitation becomes particularly relevant in scenarios where the attributes captured during data acquisition vary across different samples.
Explainable Techniques for Analyzing Flow Cytometry Cell Transformers
Explainability for Deep Learning Models is especially important for clinical applications, where decisions of automated systems have far-reaching consequences.
Automated Identification of Cell Populations in Flow Cytometry Data with Transformers
Acute Lymphoblastic Leukemia (ALL) is the most frequent hematologic malignancy in children and adolescents.
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