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Found 8 papers, 1 papers with code
Unsupervised Approaches for Out-Of-Distribution Dermoscopic Lesion Detection
There are limited works showing the efficacy of unsupervised Out-of-Distribution (OOD) methods on complex medical data.
Variation is the Norm: Brain State Dynamics Evoked By Emotional Video Clips
For the last several decades, emotion research has attempted to identify a "biomarker" or consistent pattern of brain activity to characterize a single category of emotion (e. g., fear) that will remain consistent across all instances of that category, regardless of individual and context.
Segmentation of Cellular Patterns in Confocal Images of Melanocytic Lesions in vivo via a Multiscale Encoder-Decoder Network (MED-Net)
We trained and tested our model on non-overlapping partitions of 117 reflectance confocal microscopy (RCM) mosaics of melanocytic lesions, an extensive dataset for this application, collected at four clinics in the US, and two in Italy.
Rate-Regularization and Generalization in VAEs
Variational autoencoders optimize an objective that combines a reconstruction loss (the distortion) and a KL term (the rate).
Can VAEs Generate Novel Examples?
This leads to the hypothesis that, for a sufficiently high capacity encoder and decoder, the VAE decoder will perform nearest-neighbor matching according to the coordinates in the latent space.
Structured Disentangled Representations
Deep latent-variable models learn representations of high-dimensional data in an unsupervised manner.
A Multiresolution Convolutional Neural Network with Partial Label Training for Annotating Reflectance Confocal Microscopy Images of Skin
We describe a new multiresolution "nested encoder-decoder" convolutional network architecture and use it to annotate morphological patterns in reflectance confocal microscopy (RCM) images of human skin for aiding cancer diagnosis.
Delineation of Skin Strata in Reflectance Confocal Microscopy Images using Recurrent Convolutional Networks with Toeplitz Attention
Reflectance confocal microscopy (RCM) is an effective, non-invasive pre-screening tool for skin cancer diagnosis, but it requires extensive training and experience to assess accurately.
