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Found 6 papers, 3 papers with code
CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance
Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples.
Ranked #1 on
Colorectal Polyps Characterization
on CRC
Generalizing to unseen domains via distribution matching
In this work, we tackle such problem by focusing on domain generalization: a formalization where the data generating process at test time may yield samples from never-before-seen domains (distributions).
Ranked #37 on
Domain Generalization
on PACS
Cross-Subject Statistical Shift Estimation for Generalized Electroencephalography-based Mental Workload Assessment
Besides shedding light on the assumptions that hold for a particular dataset, the estimates of statistical shifts obtained with the proposed approach can be used for investigating other aspects of a machine learning pipeline, such as quantitatively assessing the effectiveness of domain adaptation strategies.
Multi-objective training of Generative Adversarial Networks with multiple discriminators
Recent literature has demonstrated promising results for training Generative Adversarial Networks by employing a set of discriminators, in contrast to the traditional game involving one generator against a single adversary.
Learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation
Afterwards, a recurrent model is trained with the goal of providing a sequence of inputs to the previously trained frames generator, thus yielding scenes which look natural.
Generalizable Adversarial Examples Detection Based on Bi-model Decision Mismatch
Non-linear binary classifiers trained on top of our proposed features can achieve a high detection rate (>90%) in a set of white-box attacks and maintain such performance when tested against unseen attacks.
