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Found 5 papers, 0 papers with code
AutoPET III Challenge: Tumor Lesion Segmentation using ResEnc-Model Ensemble
To address this challenge, We trained a 3D Residual encoder U-Net within the no new U-Net framework, aiming to generalize the performance of automatic lesion segmentation of whole body PET/CT scans, across different tracers and clinical sites.
FUSION: Fully Unsupervised Test-Time Stain Adaptation via Fused Normalization Statistics
Stain variation, defined as a chromatic difference between the source and the target, is caused by varying characteristics during staining, resulting in a distribution shift and poor performance on the target.
ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images
The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions.
Optic Disc, Cup and Fovea Detection from Retinal Images Using U-Net++ with EfficientNet Encoder
The accurate detection of retinal structures like an optic disc (OD), cup, and fovea is crucial for the analysis of Age-related Macular Degeneration (AMD), Glaucoma, and other retinal conditions.
Ranked #1 on
Optic Cup Segmentation
on REFUGE Challenge
Histopathological Stain Transfer using Style Transfer Network with Adversarial Loss
A deep learning model was trained on this stain and the rest of the images were transferred to it using the corresponding stain transfer generator network.
