medical image segmentation

Segmentation with mixed supervision: Confidence maximization helps knowledge distillation

Despite achieving promising results in a breadth of medical image segmentation tasks, deep neural networks require large training datasets with pixel-wise annotations. Obtaining these curated datasets is a cumbersome process which limits the …

GAMMA Challenge:Glaucoma grAding from Multi-Modality imAges

Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both two modalities of images have prominent biomarkers to indicate glaucoma suspected. Clinically, it is often recommended …

Our results on MICCAI2021 GAMMA contest : Top 1 in two tasks

Our team, DIAGNOS-ETS, achieved great performance on MICCAI2021 GAMMA contest. Here are our results on the three tasks: Top 1 : Macular fovea localization Top 1 : Optic disc/cup segmentation Top 8 : Multi-modal glaucoma grading The total number of teams in this contest : 556 .

The hidden label-marginal biases of segmentation losses

Most segmentation losses are arguably variants of the Cross-Entropy (CE) or Dice losses. In the abundant segmentation literature, there is no clear consensus as to which of these losses is a better choice, with varying performances for each across …