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Found 13 papers, 5 papers with code
Anomaly Detection in Satellite Videos using Diffusion Models
The definition of anomaly detection is the identification of an unexpected event.
Deep Learning-Derived Optimal Aviation Strategies to Control Pandemics
The COVID-19 pandemic has affected countries across the world, demanding drastic public health policies to mitigate the spread of infection, leading to economic crisis as a collateral damage.
PICASO: Permutation-Invariant Cascaded Attentional Set Operator
This is in part due to the increasing number of important tasks such as meta-learning, clustering, and anomaly detection that are defined on set inputs.
First arrival picking using U-net with Lovasz loss and nearest point picking method
Similar to \cite{wu2019semi}, we use U-net to perform the segmentation as it is proven to be state-of-the-art in many image segmentation tasks.
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.
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.
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.
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.
DECAPS: Detail-Oriented Capsule Networks
We provide extensive experiments on the CheXpert and RSNA Pneumonia datasets to validate the effectiveness of DECAPS.
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.
Fast CapsNet for Lung Cancer Screening
We show that CapsNets significantly outperforms CNNs when the number of training samples is small.
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.
Unsupervised Cross-Modal Synthesis of Subject-Specific Scans
Our experiments on generating T1-MRI brain scans from T2-MRI and vice versa demonstrate that the synthesis capability of the proposed unsupervised approach is comparable to various state-of-the-art supervised approaches in the literature.
