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Found 18 papers, 6 papers with code
Uformer: A UNet-Transformer fused robust end-to-end deep learning framework for real-time denoising of lung sounds
The proposed model showed an average SNR improvement of 16. 51 dB when evaluated with -12 dB LS signals.
A Web-based Mpox Skin Lesion Detection System Using State-of-the-art Deep Learning Models Considering Racial Diversity
To address this challenge, we present the "Mpox Skin Lesion Dataset Version 2. 0 (MSLD v2. 0)" as a follow-up to the previously released openly accessible dataset, one of the first datasets containing mpox lesion images.
Learning to Generalize towards Unseen Domains via a Content-Aware Style Invariant Model for Disease Detection from Chest X-rays
Additionally, we leverage consistency regularizations on global semantic features and predictive distributions from with and without style-perturbed versions of the same CXR to tweak the model's sensitivity toward content markers for accurate predictions.
SpectNet : End-to-End Audio Signal Classification Using Learnable Spectrograms
In this paper, we present SpectNet, an integrated front-end layer that extracts spectrogram features within a CNN architecture that can be used for audio pattern recognition tasks.
ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification
Next, we develop a novel attention-based classification model that combines information from the estimated anatomical prior and automatically extracted chest region of interest (ROI) masks to provide attention to the feature maps generated from a deep convolution network.
BON: An extended public domain dataset for human activity recognition
Body-worn first-person vision (FPV) camera enables to extract a rich source of information on the environment from the subject's viewpoint.
Monkeypox Skin Lesion Detection Using Deep Learning Models: A Feasibility Study
Clinical diagnosis of monkeypox in an early stage is challenging due to its similarity with chickenpox and measles.
Anatomy-XNet: An Anatomy Aware Convolutional Neural Network for Thoracic Disease Classification in Chest X-rays
We adopt a semi-supervised learning method by utilizing available small-scale organ-level annotations to locate the anatomy regions in large-scale datasets where the organ-level annotations are absent.
Ranked #15 on
Multi-Label Classification
on CheXpert
Heart Sound Classification Considering Additive Noise and Convolutional Distortion
Based on the analysis, we propose a combination of linear and logarithmic spectrogram-image features.
Respiratory Distress Detection from Telephone Speech using Acoustic and Prosodic Features
To capture these variations, we utilize a set of well-known acoustic and prosodic features with a Support Vector Machine (SVM) classifier for detecting the presence of respiratory distress.
A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds using EMD-CWT-based Hybrid Scalogram
Listening to lung sounds through auscultation is vital in examining the respiratory system for abnormalities.
SegCodeNet: Color-Coded Segmentation Masks for Activity Detection from Wearable Cameras
In comparison to a single-stream network, the proposed two-stream method achieves an absolute improvement of $14. 366\%$ and $10. 324\%$ for averaged F1 score and accuracy, respectively, when average results are compared for three different frame sizes $224\times224$, $112\times112$, and $64\times64$.
Privacy-Aware Activity Classification from First Person Office Videos
In the advent of wearable body-cameras, human activity classification from First-Person Videos (FPV) has become a topic of increasing importance for various applications, including in life-logging, law-enforcement, sports, workplace, and healthcare.
Towards Domain Invariant Heart Sound Abnormality Detection using Learnable Filterbanks
Cardiac auscultation is the most practiced non-invasive and cost-effective procedure for the early diagnosis of heart diseases.
Signal Processing
X-Ray Image Compression Using Convolutional Recurrent Neural Networks
To the best of our knowledge, this is the first reported evaluation on using a deep convolutional RNN for medical image compression.
End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets
The experimental results demonstrate the superiority of the proposed network compared to the best existing method, providing a relative improvement in epoch-wise average accuracy of 6. 8% and 6. 3% on the household data and multi-source data, respectively.
An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classification
In this work, we propose an ensemble of classifiers to distinguish between various degrees of abnormalities of the heart using Phonocardiogram (PCG) signals acquired using digital stethoscopes in a clinical setting, for the INTERSPEECH 2018 Computational Paralinguistics (ComParE) Heart Beats SubChallenge.
Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detection
In this work, we propound a novel CNN architecture that integrates the front-end bandpass filters within the network using time-convolution (tConv) layers, which enables the FIR filter-bank parameters to become learnable.
