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Found 7 papers, 4 papers with code
Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology
The interactions between tumor cells and the tumor microenvironment (TME) dictate therapeutic efficacy of radiation and many systemic therapies in breast cancer.
Massively Annotated Datasets for Assessment of Synthetic and Real Data in Face Recognition
Additionally, we further inspect the differences between real and synthetic datasets on the attribute set.
Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection
Over time they have become simpler to perform and more realistic, as such, the usage of deep learning systems to detect these attacks has grown.
Ranked #1 on
Face Morphing Attack Detection
on FRLL-Morphs
Causality-Inspired Taxonomy for Explainable Artificial Intelligence
As two sides of the same coin, causality and explainable artificial intelligence (xAI) were initially proposed and developed with different goals.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement
Morphing attacks are one of the many threats that are constantly affecting deep face recognition systems.
SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data
The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries.
A survey on attention mechanisms for medical applications: are we moving towards better algorithms?
This paper concludes with a critical analysis of the claims and potentialities presented in the literature about attention mechanisms and proposes future research lines in medical applications that may benefit from these frameworks.
