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Found 5 papers, 1 papers with code
Epileptic Seizure Classification Using Combined Labels and a Genetic Algorithm
The features are compared using an original NB classifier in the first model.
CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor Segmentation
Furthermore, driven by the scarcity of brain tumor data, we investigate the Noisy Student method for segmentation tasks.
A new semi-supervised self-training method for lung cancer prediction
This study presents a complete end-to-end scheme to detect and classify lung nodules using the state-of-the-art Self-training with Noisy Student method on a comprehensive CT lung screening dataset of around 4, 000 CT scans.
Lung Nodule Classification using Deep Local-Global Networks
In this paper, we propose a novel method to predict the malignancy of nodules that have the capability to analyze the shape and size of a nodule using a global feature extractor, as well as the density and structure of the nodule using a local feature extractor.
Ranked #6 on
Lung Nodule Classification
on LIDC-IDRI
