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Found 4 papers, 3 papers with code
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.
ECSIC: Epipolar Cross Attention for Stereo Image Compression
Our proposed method compresses the left and right images in a joint manner by exploiting the mutual information between the images of the stereo image pair using a novel stereo cross attention (SCA) module and two stereo context modules.
SASIC: Stereo Image Compression With Latent Shifts and Stereo Attention
We propose a learned method for stereo image compression that leverages the similarity of the left and right images in a stereo pair due to overlapping fields of view.
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.
