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Found 26 papers, 2 papers with code
DiCoM -- Diverse Concept Modeling towards Enhancing Generalizability in Chest X-Ray Studies
In this context, we introduce Diverse Concept Modeling (DiCoM), a novel self-supervised training paradigm that leverages a student teacher framework for learning diverse concepts and hence effective representation of the CXR data.
Personality Trait Recognition using ECG Spectrograms and Deep Learning
This paper presents an innovative approach to recognizing personality traits using deep learning (DL) methods applied to electrocardiogram (ECG) signals.
Harmonization Across Imaging Locations(HAIL): One-Shot Learning for Brain MRI
To prevent hallucination in medical imaging, such as magnetic resonance images (MRI) of the brain, we propose a one-shot learning method where we utilize neural style transfer for harmonization.
SelfFed: Self-supervised Federated Learning for Data Heterogeneity and Label Scarcity in IoMT
We perform our experimental analysis on publicly available medical imaging datasets and show that our proposed SelfFed framework performs better when compared to existing baselines concerning non-independent and identically distributed (IID) data and label scarcity.
The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI
Clinical monitoring of metastatic disease to the brain can be a laborious and time-consuming process, especially in cases involving multiple metastases when the assessment is performed manually.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting
The challenge is organized as part of the BraTS 2023 challenge hosted at the MICCAI 2023 conference in Vancouver, Canada.
The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma
Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality.
Upper Limb Movement Execution Classification using Electroencephalography for Brain Computer Interface
We propose a method to classify four ME classes for different subjects using spectrograms of the EEG data through pre-trained deep learning (DL) models.
SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model
However, the traditional diagnostic tool design methods based on supervised learning are burdened by the need to provide training data annotation, which should be of good quality for better clinical outcomes.
Ranked #1 on
Semantic Segmentation
on Montgomery County X-ray Set
Motor imagery classification using EEG spectrograms
Our novel approach for the classification of upper limb movements using pre-trained DL algorithms and spectrograms has achieved significantly improved results for seven movement classes.
SB-SSL: Slice-Based Self-Supervised Transformers for Knee Abnormality Classification from MRI
The availability of large scale data with high quality ground truth labels is a challenge when developing supervised machine learning solutions for healthcare domain.
A Multimodal Perceived Stress Classification Framework using Wearable Physiological Sensors
A perceived stress scale (PSS) questionnaire is used to record the stress of participants, which is then used to assign stress labels (two- and three classes).
Human Stress Assessment: A Comprehensive Review of Methods Using Wearable Sensors and Non-wearable Techniques
This paper presents a comprehensive review of methods covering significant subjective and objective human stress detection techniques available in the literature.
M-Net with Bidirectional ConvLSTM for Cup and Disc Segmentation in Fundus Images
Our proposed model segments cup and disc regions based on which the abnormalities in cup to disc ratio can be observed.
Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data
Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory.
Variational Capsule Encoder
We propose a novel capsule network based variational encoder architecture, called Bayesian capsules (B-Caps), to modulate the mean and standard deviation of the sampling distribution in the latent space.
Brain Tumor Survival Prediction using Radiomics Features
Thus, accurate survival prognosis is an important step in treatment planning.
Deep Learning for Musculoskeletal Image Analysis
The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging(MRI), and ultrasound) and their precise analysis by expert radiologists.
A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology
Artificial intelligence (AI) enabled radiomics has evolved immensely especially in the field of oncology.
Electroencephalography based Classification of Long-term Stress using Psychological Labeling
Stress research is a rapidly emerging area in thefield of electroencephalography (EEG) based signal processing. The use of EEG as an objective measure for cost effective andpersonalized stress management becomes important in particularsituations such as the non-availability of mental health facilities. In this study, long-term stress is classified using baseline EEGsignal recordings.
Classification of Perceived Human Stress using Physiological Signals
In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals.
Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals
The response to this enhanced multimedia content (mulsemedia) is evaluated in terms of the appreciation/emotion by using human brain signals.
Medical Image Analysis using Convolutional Neural Networks: A Review
Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features.
Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network
A patch based approach along with an inception module is used for training the deep network by extracting two co-centric patches of different sizes from the input images.
Medical Image Retrieval using Deep Convolutional Neural Network
The learned features and the classification results are used to retrieve medical images.
