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Found 7 papers, 0 papers with code
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.
Patch-based Non-Local Bayesian Networks for Blind Confocal Microscopy Denoising
The performance of our proposed method is evaluated on confocal microscopy images with real noise Poisson-Gaussian noise.
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.
Image Super Resolution via Bilinear Pooling: Application to Confocal Endomicroscopy
We compare the efficacy of our method to 11 other existing single image super resolution techniques that compensate for the reduction in image quality caused by the necessity of endomicroscope miniaturization.
Can Deep Learning Relax Endomicroscopy Hardware Miniaturization Requirements?
In this work, we demonstrate that software-based techniques can be used to recover lost information due to endomicroscopy hardware miniaturization and reconstruct images of higher resolution.
iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning
Tolerance to image variations (e. g. translation, scale, pose, illumination, background) is an important desired property of any object recognition system, be it human or machine.
What can we learn about CNNs from a large scale controlled object dataset?
Tolerance to image variations (e. g. translation, scale, pose, illumination) is an important desired property of any object recognition system, be it human or machine.
