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Found 17 papers, 1 papers with code
M3BUNet: Mobile Mean Max UNet for Pancreas Segmentation on CT-Scans
Segmenting organs in CT scan images is a necessary process for multiple downstream medical image analysis tasks.
Multimodal Neuroimaging Attention-Based architecture for Cognitive Decline Prediction
The proposed MNA-net is tested in OASIS-3 dataset and is able to predict CN individuals who converted to MCI or AD with an accuracy of 83%, true negative rate of 80%, and true positive rate of 86%.
COVID-19 Detection System: A Comparative Analysis of System Performance Based on Acoustic Features of Cough Audio Signals
A wide range of respiratory diseases, such as cold and flu, asthma, and COVID-19, affect people's daily lives worldwide.
SCOL: Supervised Contrastive Ordinal Loss for Abdominal Aortic Calcification Scoring on Vertebral Fracture Assessment Scans
We develop a Dual-encoder Contrastive Ordinal Learning (DCOL) framework that learns the contrastive ordinal representation at global and local levels to improve the feature separability and class diversity in latent space among the AAC-24 genera.
Classification of sleep stages from EEG, EOG and EMG signals by SSNet
The performance of our proposed model is evaluated by using two public datasets Sleep-EDF Expanded dataset and ISRUC-Sleep dataset.
Patient Independent Interictal Epileptiform Discharge Detection
This is implemented using raw EEG data, sourced from Temple University Hospital's EEG Epilepsy Corpus, and is found to outperform the current state of the art model for IED detection across the same dataset.
Human Gesture and Gait Analysis for Autism Detection
Autism diagnosis presents a major challenge due to the vast heterogeneity of the condition and the elusive nature of early detection.
Learning Sparse Temporal Video Mapping for Action Quality Assessment in Floor Gymnastics
Athlete performance measurement in sports videos requires modeling long sequences since the entire spatio-temporal progression contributes dominantly to the performance.
MP-SeizNet: A Multi-Path CNN Bi-LSTM Network for Seizure-Type Classification Using EEG
The EEG data is fed to our proposed model in two different representations.
Vision Transformer Based Model for Describing a Set of Images as a Story
Visual Story-Telling is the process of forming a multi-sentence story from a set of images.
Ranked #25 on
Visual Storytelling
on VIST
Evaluating BERT-based Pre-training Language Models for Detecting Misinformation
The result of the proposed technique has been compared with the state-of-the-art techniques on the same datasets.
Wavelet-Based Multi-Class Seizure Type Classification System
Epilepsy is one of the most common brain diseases that affect more than 1\% of the world's population.
A Survey of Neural Trojan Attacks and Defenses in Deep Learning
It provides a comprehensible gateway to the broader community to understand the recent developments in Neural Trojans.
BERT based classification system for detecting rumours on Twitter
These approaches rely on feature extraction to obtain both content and context features from the text of tweets to distinguish rumours and non-rumours.
Estimating galaxy masses from kinematics of globular cluster systems: a new method based on deep learning
Then we use the CNN to predict the total masses of galaxies (i. e., test the CNN) for the totally unknown dataset that is not used in training the CNN.
Automatic detection of abnormal EEG signals using wavelet feature extraction and gradient boosting decision tree
In this paper, we present an automatic binary classification framework for brain signals in multichannel EEG recordings.
A Comparative Study of Techniques of Distant Reconstruction of Displacement Fields by using DISTRESS Simulator
We investigated the Basic DIC and a commercial software VIC 2D, both based on displacement field reconstruction with post processing strain determination based on numerical differentiation.
