Machine Learning

Anomaly guided segmentation: Introducing semantic context for lesion segmentation in retinal OCT using weak context supervision from anomaly detection

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.

Semi-supervised learning with Noisy Students improves domain generalization in optic disc and cup segmentation in uncropped fundus images

The paper evaluates domain generalization strategies for optic disc and cup segmentation in fundus images, highlighting issues with existing methods when applied to uncropped images, and proposes a semi-supervised learning approach based on the Noisy Student framework to improve performance across diverse datasets.

Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation

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.

GAMMA challenge: Glaucoma grAding from Multi-Modality imAges

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.

Learning normal asymmetry representations for homologous brain structures

The paper presents a novel method for learning normal asymmetry patterns in brain structures. It accurately characterizes normal asymmetries and detects pathological alterations without relying on diseased cases for training. The approach shows promise in improving the identification of neurodegenerative conditions..

NORHA: A NORmal Hippocampal Asymmetry deviation index based on one-class novelty detection and 3D shape features

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.

Postoperative vault prediction for phakic implantable collamer lens surgery: the LASSO formulae

We developed a new set of ICL sizing formulae based on LASSO regression, OCT measurements and ocular biometry data.

Machine learning for filtering out false positive grey matter atrophies in single subject voxel based morphometry: A simulation based study

We used SVMs to filter out false positive detections causes by single subject VBM when applied for discovering abnormalities in MRI scans.

Towards a Glaucoma Risk Index Based on Simulated Hemodynamics from Fundus Images

We designed a method to summarize hemodynamic parameters obtained by 0D simulations so that they can be applied for glaucoma detection. We observed certain correlation between glaucoma and these hemodynamic features.

Retinal blood vessel segmentation in high resolution fundus photographs using automated feature parameter estimation

We developed a simple linear regression model that is able to estimate the hyperparameters of a fully-connected CRF model for blood vessel segmentation in fundus images.