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Found 7 papers, 0 papers with code
Enhanced Depth Estimation and 3D Geometry Reconstruction using Bayesian Helmholtz Stereopsis with Belief Propagation
We propose a new smoothness function utilizing the normal field integration method for refined depth estimation within the Bayesian framework.
Swin-Tempo: Temporal-Aware Lung Nodule Detection in CT Scans as Video Sequences Using Swin Transformer-Enhanced UNet
However, identifying lung nodules poses significant challenges for radiologists, who rely heavily on their expertise for accurate diagnosis.
Deep Bayesian Active Learning, A Brief Survey on Recent Advances
Deep Bayesian active learning frameworks and generally any Bayesian active learning settings, provide practical consideration in the model which allows training with small data while representing the model uncertainty for further efficient training.
A New Framework For Spatial Modeling And Synthesis of Genome Sequence
This paper provides a framework in order to statistically model sequences from human genome, which is allowing a formulation to synthesize gene sequences.
Graph-Based Method for Anomaly Prediction in Brain Network
Irregular graph deep learning applications have widely spread to understanding human cognitive functions that are linked to gene expression and related distributed spatial patterns, because the neuronal networks of the brain can hold dynamically a variety of brain solutions with different activity patterns and functional connectivity, these applications might also be involved with both node-centric and graph-centric tasks.
Semi-Supervised Hierarchical Semantic Object Parsing
Models based on Convolutional Neural Networks (CNNs) have been proven very successful for semantic segmentation and object parsing that yield hierarchies of features.
Sparse Estimation with Generalized Beta Mixture and the Horseshoe Prior
In this paper, the use of the Generalized Beta Mixture (GBM) and Horseshoe distributions as priors in the Bayesian Compressive Sensing framework is proposed.
