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Found 11 papers, 5 papers with code
Lung Cancer Screening Using Adaptive Memory-Augmented Recurrent Networks
In this paper, we investigate the effectiveness of deep learning techniques for lung nodule classification in computed tomography scans.
Text-Independent Speaker Verification Using Long Short-Term Memory Networks
For speaker verification, at first, a background model must be created for speaker representation.
Fast CapsNet for Lung Cancer Screening
We show that CapsNets significantly outperforms CNNs when the number of training samples is small.
DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks
In this paper, we develop a theoretical framework to approximate Bayesian inference for DNNs by imposing a Bernoulli distribution on the model weights.
DVNet: A Memory-Efficient Three-Dimensional CNN for Large-Scale Neurovascular Reconstruction
Maps of brain microarchitecture are important for understanding neurological function and behavior, including alterations caused by chronic conditions such as neurodegenerative disease.
Radiologist-Level COVID-19 Detection Using CT Scans with Detail-Oriented Capsule Networks
The network then uses the activation maps to focus on regions of interest and combines both coarse and fine-grained representations of the data.
StyPath: Style-Transfer Data Augmentation For Robust Histology Image Classification
We also generated Grad-CAM representations of the results which were assessed by an experienced nephropathologist; we used this qualitative analysis to elucidate on the assumptions being made by each model.
DECAPS: Detail-Oriented Capsule Networks
We provide extensive experiments on the CheXpert and RSNA Pneumonia datasets to validate the effectiveness of DECAPS.
Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification
Meta-learning aims to deliver an adaptive model that is sensitive to these underlying distribution changes, but requires many tasks during the meta-training process.
Kidney Level Lupus Nephritis Classification using Uncertainty Guided Bayesian Convolutional Neural Networks
The kidney biopsy based diagnosis of Lupus Nephritis (LN) is characterized by low inter-observer agreement, with misdiagnosis being associated with increased patient morbidity and mortality.
Trans-Caps: Transformer Capsule Networks with Self-attention Routing
Capsule Networks (CapsNets) have shown to be a promising alternative to Convolutional Neural Networks (CNNs) in many computer vision tasks, due to their ability to encode object viewpoint variations.
