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Found 31 papers, 14 papers with code
FedBlock: A Blockchain Approach to Federated Learning against Backdoor Attacks
Second, due to its distributed nature, FL might encounter backdoor attacks by participating clients.
MSTAR: Multi-Scale Backbone Architecture Search for Timeseries Classification
Most of the previous approaches to Time Series Classification (TSC) highlight the significance of receptive fields and frequencies while overlooking the time resolution.
Learning to Estimate Critical Gait Parameters from Single-View RGB Videos with Transformer-Based Attention Network
Musculoskeletal diseases and cognitive impairments in patients lead to difficulties in movement as well as negative effects on their psychological health.
MPCNN: A Novel Matrix Profile Approach for CNN-based Sleep Apnea Classification
Sleep apnea (SA) is a significant respiratory condition that poses a major global health challenge.
TransReg: Cross-transformer as auto-registration module for multi-view mammogram mass detection
Screening mammography is the most widely used method for early breast cancer detection, significantly reducing mortality rates.
Evaluating the impact of an explainable machine learning system on the interobserver agreement in chest radiograph interpretation
We conducted a prospective study to measure the clinical impact of an explainable machine learning system on interobserver agreement in chest radiograph interpretation.
Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation
The work discusses the use of machine learning algorithms for anomaly detection in medical image analysis and how the performance of these algorithms depends on the number of annotators and the quality of labels.
Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directions
Federated learning (FL) is a machine learning (ML) approach that allows the use of distributed data without compromising personal privacy.
FedDCT: Federated Learning of Large Convolutional Neural Networks on Resource Constrained Devices using Divide and Collaborative Training
FedDCT reduces the memory requirements and allows low-end devices to participate in FL.
Enhancing Few-shot Image Classification with Cosine Transformer
This might result in a significant difference between support and query samples, therefore undermining the performance of few-shot algorithms.
Multi-stream Fusion for Class Incremental Learning in Pill Image Classification
From this framework, we consider color-specific information of pill images as a guidance stream and devise an approach, namely "Color Guidance with Multi-stream intermediate fusion"(CG-IMIF) for solving CIL pill image classification task.
Learning to diagnose common thorax diseases on chest radiographs from radiology reports in Vietnamese
Our best model (CheXpert-pretrained EfficientNet-B2) yields an F1-score of 0. 6989 (95% CI 0. 6740, 0. 7240), AUC of 0. 7912, sensitivity of 0. 7064 and specificity of 0. 8760 for the abnormal diagnosis in general.
Enhancing Deep Learning-based 3-lead ECG Classification with Heartbeat Counting and Demographic Data Integration
Nowadays, an increasing number of people are being diagnosed with cardiovascular diseases (CVDs), the leading cause of death globally.
Detecting COVID-19 from digitized ECG printouts using 1D convolutional neural networks
Experiments on the COVID-19 ECG images dataset demonstrate that the proposed digitization method is able to capture correctly the original signals, with a mean absolute error of 28. 11 ms. Our proposed 1D-CNN model, which is trained on the digitized ECG signals, allows identifying individuals with COVID-19 and other subjects accurately, with classification accuracies of 98. 42%, 95. 63%, and 98. 50% for classifying COVID-19 vs. Normal, COVID-19 vs. Abnormal Heartbeats, and COVID-19 vs. other classes, respectively.
Video-based Human Action Recognition using Deep Learning: A Review
Human action recognition is an important application domain in computer vision.
An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph
For the localization task with 14 types of lesions, our free-response receiver operating characteristic (FROC) analysis showed that the VinDr-CXR achieved a sensitivity of 80. 2% at the rate of 1. 0 false-positive lesion identified per scan.
Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices
In particular, deep convolutional neural networks (D-CNNs) have been key players and were adopted by the medical imaging community to assist clinicians and medical experts in disease diagnosis and treatment.
LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification
In clinical practices and most of the current research, standard 12-lead ECG is mainly used.
PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children
To the best of our knowledge, this is the first and largest pediatric CXR dataset containing lesion-level annotations and image-level labels for the detection of multiple findings and diseases.
Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis
As a result, the labeled data may contain a variety of human biases with a high rate of disagreement among annotators, which significantly affect the performance of supervised machine learning algorithms.
A Novel Transparency Strategy-based Data Augmentation Approach for BI-RADS Classification of Mammograms
Image augmentation techniques have been widely investigated to improve the performance of deep learning (DL) algorithms on mammography classification tasks.
VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography
Mammography, or breast X-ray, is the most widely used imaging modality to detect cancer and other breast diseases.
Phase Recognition in Contrast-Enhanced CT Scans based on Deep Learning and Random Sampling
This work aims at developing and validating a precise, fast multi-phase classifier to recognize three main types of contrast phases in abdominal CT scans.
A novel multi-view deep learning approach for BI-RADS and density assessment of mammograms
The experimental results demonstrate that the proposed approach outperforms the single-view classification approach on two benchmark datasets by huge F1-score margins (+5% on the internal dataset and +10% on the DDSM dataset).
DICOM Imaging Router: An Open Deep Learning Framework for Classification of Body Parts from DICOM X-ray Scans
This challenge raises the need for an automated and efficient approach to classifying body parts from X-ray scans.
Learning to Automatically Diagnose Multiple Diseases in Pediatric Chest Radiographs Using Deep Convolutional Neural Networks
Chest radiograph (CXR) interpretation in pediatric patients is error-prone and requires a high level of understanding of radiologic expertise.
VinDr-RibCXR: A Benchmark Dataset for Automatic Segmentation and Labeling of Individual Ribs on Chest X-rays
We introduce a new benchmark dataset, namely VinDr-RibCXR, for automatic segmentation and labeling of individual ribs from chest X-ray (CXR) scans.
VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs
It demonstrates an area under the receiver operating characteristic curve (AUROC) of 88. 61% (95% CI 87. 19%, 90. 02%) for the image-level classification task and a mean average precision (mAP@0. 5) of 33. 56% for the lesion-level localization task.
VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations
Most of the existing chest X-ray datasets include labels from a list of findings without specifying their locations on the radiographs.
Interpreting Chest X-rays via CNNs that Exploit Hierarchical Disease Dependencies and Uncertainty Labels
The chest X-rays (CXRs) is one of the views most commonly ordered by radiologists (NHS), which is critical for diagnosis of many different thoracic diseases.
Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels
The performance is on average better than 2. 6 out of 3 other individual radiologists with a mean AUC of 0. 930, which ranks first on the CheXpert leaderboard at the time of writing this paper.
Ranked #2 on
Multi-Label Classification
on CheXpert
