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Found 5 papers, 1 papers with code
A Survey on Deep Learning for Skin Lesion Segmentation
We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance).
D-LEMA: Deep Learning Ensembles from Multiple Annotations -- Application to Skin Lesion Segmentation
To this end, we propose an ensemble of Bayesian fully convolutional networks (FCNs) for the segmentation task by considering two major factors in the aggregation of multiple ground truth annotations: (1) handling contradictory annotations in the training data originating from inter-annotator disagreements and (2) improving confidence calibration through the fusion of base models' predictions.
WhiteNNer-Blind Image Denoising via Noise Whiteness Priors
Specifically, by using total variation and piecewise constancy priors along with noise whiteness priors such as auto-correlation and stationary losses, our network learns to decouple an input noisy image into the underlying signal and noise components.
Learning to Segment Skin Lesions from Noisy Annotations
Deep convolutional neural networks have driven substantial advancements in the automatic understanding of images.
Star Shape Prior in Fully Convolutional Networks for Skin Lesion Segmentation
Semantic segmentation is an important preliminary step towards automatic medical image interpretation.
