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Found 9 papers, 2 papers with code
AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challenge
Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible.
Deep Dirichlet uncertainty for unsupervised out-of-distribution detection of eye fundus photographs in glaucoma screening
The development of automatic tools for early glaucoma diagnosis with color fundus photographs can significantly reduce the impact of this disease.
HEROHE Challenge: assessing HER2 status in breast cancer without immunohistochemistry or in situ hybridization
Breast cancer is the most common malignancy in women, being responsible for more than half a million deaths every year.
DR$\vert$GRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images
We show that high QWK values occur for images with low prediction uncertainty, thus indicating that this uncertainty is a valid measure of the predictions' quality.
Did you miss it? Automatic lung nodule detection combined with gaze information improves radiologists' screening performance
Likewise, combining the findings of radiologist with the detection algorithm only for low fixation regions still significantly improves the detection sensitivity without increasing the number of false-positives.
iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network
We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images.
UOLO - automatic object detection and segmentation in biomedical images
We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images.
BACH: Grand Challenge on Breast Cancer Histology Images
From the submitted algorithms it was possible to push forward the state-of-the-art in terms of accuracy (87%) in automatic classification of breast cancer with histopathological images.
Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis
Specifically, we apply the \emph{shades of gray} color constancy technique to color-normalize the entire training set of images, while retaining the estimated illuminants.
