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Found 4 papers, 2 papers with code
Deep Learning Architectures for Diagnosis of Diabetic Retinopathy
For many years, convolutional neural networks dominated the field of computer vision, not least in the medical field, where problems such as image segmentation were addressed by such networks as the U-Net.
Ranked #6 on
Retinal Vessel Segmentation
on DRIVE
(F1 score metric)
Psychophysics of Artificial Neural Networks Questions Classical Hue Cancellation Experiments
In fact, the specific cancellation lights (and not the network architecture) are key to obtain human-like curves: results show that the classical choice of the lights is the one that leads to the best (more human-like) result, and any other choices lead to progressively different spectral sensitivities.
Analysis of Deep Image Quality Models
In this work, we perform a thorough analysis of the perceptual properties of pre-trained nets (particularly their ability to predict image quality) by isolating different factors: the goal (the function), the data (learning environment), the architecture, and the readout: selected layer(s), fine-tuning of channel relevance, and use of statistical descriptors as opposed to plain readout of responses.
Neural Networks with Divisive normalization for image segmentation with application in cityscapes dataset
In addition to the results on the Cityscapes and Foggy Cityscapes datasets, we explain these advantages through visualization of the responses: the equalization induced by the divisive normalization leads to more invariant features to local changes in contrast and illumination.
