Incorporating weak anomaly labels into standard segmentation models improves lesion segmentation results without requiring extra manual labels, enhancing the potential of anomaly guided segmentation for retinal optical coherence tomography scans.
This paper introduces PALM, an open fundus imaging dataset for pathological myopia recognition, featuring 1200 images with associated labels for pathologic myopia category and manual annotations of optic disc, fovea position, and lesions like patchy retinal atrophy and retinal detachment, aiding in automated diagnostic tools development.
The GAMMA Challenge addresses the need for multi-modality glaucoma grading, providing a dataset with both 2D fundus images and 3D OCT volumes, inviting algorithm development and evaluation, leading to practical insights for clinical diagnosis.
NORHA is a novel index for quantifying hippocampal asymmetries in neurodegenerative conditions. It shows promise as a biomarker for detecting unilateral abnormalities, such as hippocampal sclerosis, and correlates positively with the functional cognitive test CDR-SB, indicating its potential in dementia diagnosis.
We develop a deep neural network that automatically extracts contextual features from patches in sketches, trained with 3D models rendered with non-photorealistic techniques. Our method is able to find dense correspondences between real world sketches!