Search Results for author:
Found 31 papers, 14 papers with code
Temporal Feature Weaving for Neonatal Echocardiographic Viewpoint Video Classification
Automated viewpoint classification in echocardiograms can help under-resourced clinics and hospitals in providing faster diagnosis and screening when expert technicians may not be available.
Enhanced Cross-Dataset Electroencephalogram-based Emotion Recognition using Unsupervised Domain Adaptation
Furthermore, PC-TTA reduces computational time by a factor of 15 compared to traditional TTA methods.
DynaSeg: A Deep Dynamic Fusion Method for Unsupervised Image Segmentation Incorporating Feature Similarity and Spatial Continuity
We provide qualitative and quantitative results on five benchmark datasets, demonstrating the efficacy of the proposed approach.
Ranked #1 on
Unsupervised Semantic Segmentation
on COCO-Stuff-27
A Dynamically Weighted Loss Function for Unsupervised Image Segmentation
This work presents an improved version of an unsupervised Convolutional Neural Network (CNN) based algorithm that uses a constant weight factor to balance between the segmentation criteria of feature similarity and spatial continuity, and it requires continuous manual adjustment of parameters depending on the degree of detail in the image and the dataset.
Cross-Database and Cross-Channel ECG Arrhythmia Heartbeat Classification Based on Unsupervised Domain Adaptation
We propose a novel technique to select confident predictions in the target domain.
Structure Preserving Cycle-GAN for Unsupervised Medical Image Domain Adaptation
SP Cycle-GAN achieved a state of the art Myocardium segmentation Dice score (DSC) of 0. 7435 for the MR to CT MM-WHS domain adaptation problem, and excelled in nearly all categories for the MM-WHS dataset.
Lightweight and Interpretable Left Ventricular Ejection Fraction Estimation using Mobile U-Net
This paper presents a deep learning based framework to automatically estimate left ventricular ejection fraction from an entire 4-chamber apical echocardiogram video.
Classification of Lung Pathologies in Neonates using Dual Tree Complex Wavelet Transform
Likewise, the proposed method achieved a maximum per-subject classification accuracy of 81. 53% with 43 DTCWT features and 3 clinical features using the balanced dataset and 64. 97% with 13 DTCWT features and 3 clinical features using the unbalanced dataset.
Pan-Tompkins++: A Robust Approach to Detect R-peaks in ECG Signals
We achieve 2. 8% and 1. 8% reduction in FP and FN, respectively, and 2. 2% increase in F-score on average across four datasets, with 33% reduction in execution time.
A Survey on Physiological Signal Based Emotion Recognition
Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as preprocessing, feature extraction, and classification.
Edge-preserving Domain Adaptation for semantic segmentation of Medical Images
Domain Adaptation is a technique to address the lack of massive amounts of labeled data in unseen environments.
Interactive segmentation using U-Net with weight map and dynamic user interactions
Interactive segmentation has recently attracted attention for specialized tasks where expert input is required to further enhance the segmentation performance.
Online unsupervised Learning for domain shift in COVID-19 CT scan datasets
When changes are made in the process of medical imaging, trained networks may not perform as well, and obtaining large amounts of expert annotations for each change in the imaging process can be time consuming and expensive.
ECG Heartbeat Classification Using Multimodal Fusion
We achieved classification accuracy of 99. 7% and 99. 2% on arrhythmia and MI classification, respectively.
Multi-level Stress Assessment from ECG in a Virtual Reality Environment using Multimodal Fusion
In this paper, we report our findings on a new study on VR stress assessment, where three stress levels are assessed.
Inertial Sensor Data To Image Encoding For Human Action Recognition
To get the maximum advantage of CNN model for Human Action Recognition (HAR) using inertial sensor data, in this paper, we use 4 types of spatial domain methods for transforming inertial sensor data to activity images, which are then utilized in a novel fusion framework.
ECG Heart-beat Classification Using Multimodal Image Fusion
In this paper, we present a novel Image Fusion Model (IFM) for ECG heart-beat classification to overcome the weaknesses of existing machine learning techniques that rely either on manual feature extraction or direct utilization of 1D raw ECG signal.
An interpretable object detection based model for the diagnosis of neonatal lung diseases using Ultrasound images
Over the last few decades, Lung Ultrasound (LUS) has been increasingly used to diagnose and monitor different lung diseases in neonates.
Motion Vector Extrapolation for Video Object Detection
Despite the continued successes of computationally efficient deep neural network architectures for video object detection, performance continually arrives at the great trilemma of speed versus accuracy versus computational resources (pick two).
GRIHA: Synthesizing 2-Dimensional Building Layouts from Images Captured using a Smart Phone
Reconstructing an indoor scene and generating a layout/floor plan in 3D or 2D is a widely known problem.
Targeted Self Supervision for Classification on a Small COVID-19 CT Scan Dataset
A total of 10 experiments are run comparing the classification performance of the proposed method of self supervision with different amounts of data.
CNN based Multistage Gated Average Fusion (MGAF) for Human Action Recognition Using Depth and Inertial Sensors
Experiments on three publicly available multimodal HAR datasets demonstrate that the proposed MGAF outperforms the previous state of the art fusion methods for depth-inertial HAR in terms of recognition accuracy while being computationally much more efficient.
Interpreting Uncertainty in Model Predictions For COVID-19 Diagnosis
COVID-19, due to its accelerated spread has brought in the need to use assistive tools for faster diagnosis in addition to typical lab swab testing.
Multidomain Multimodal Fusion For Human Action Recognition Using Inertial Sensors
One of the major reasons for misclassification of multiplex actions during action recognition is the unavailability of complementary features that provide the semantic information about the actions.
Towards Improved Human Action Recognition Using Convolutional Neural Networks and Multimodal Fusion of Depth and Inertial Sensor Data
The recognition accuracies of each modality, depth data alone and sensor data alone are also calculated and compared with fusion based accuracies to highlight the fact that fusion of modalities yields better results than individual modalities.
An Efficient Confidence Measure-Based Evaluation Metric for Breast Cancer Screening Using Bayesian Neural Networks
We show that by providing the medical practitioners with a tool to tune two hyperparameters of the Bayesian neural network, namely, fraction of sampled number of networks and minimum probability, the framework can be adapted as needed by the domain expert.
Multi-level Stress Assessment Using Multi-domain Fusion of ECG Signal
To get the maximum advantage of fusing diferent domains, we introduce a dataset with multiple stress levels and then classify these levels using a novel deep learning approach by converting ECG signal into signal images based on R-R peaks without any feature extraction.
Facial Expression Recognition Under Partial Occlusion from Virtual Reality Headsets based on Transfer Learning
In this paper we attempt to overcome these issues and focus on facial expression recognition in presence of a severe occlusion where the user is wearing a head-mounted display in a VR setting.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Locality Guided Neural Networks for Explainable Artificial Intelligence
Heavily motivated by Self-Organizing Map (SOM), the goal is to enforce a local topology on each layer of a deep network such that neighbouring neurons are highly correlated with each other.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Human Action Recognition Using Deep Multilevel Multimodal (M2) Fusion of Depth and Inertial Sensors
CNNs are trained on input images of each modality to learn low-level, high-level and complex features.
Towards Alzheimer's Disease Classification through Transfer Learning
Detection of Alzheimer's Disease (AD) from neuroimaging data such as MRI through machine learning have been a subject of intense research in recent years.
