Deep learning aids in autism diagnosis and rehabilitation using neuroimaging data.
problem Challenges in automated detection and rehabilitation of ASD using neuroimaging data.
method Deep learning techniques applied to neuroimaging data for ASD diagnosis and rehabilitation.
result Deep learning improves accuracy in ASD diagnosis and rehabilitation.
A new COVID-19 CT dataset helps develop AI diagnosis models.
problem Lack of publicly available COVID-19 CT datasets due to privacy issues.
method Built an open-sourced COVID-CT dataset and developed AI diagnosis methods.
result Developed AI diagnosis models achieving high accuracy and performance.
AI framework diagnoses Parkinson's disease with 100% accuracy.
problem Expertise-demanding medical imaging procedures for Parkinson's disease diagnosis.
method End-to-end, multi-modality diagnosis framework using T1-MRI and 11C-CFT PET.
result 100% accuracy in PD/NL classification.
ALPODS AI diagnoses high-dimensional biomedical data with human-understandable explanations.
problem AI decisions in high-dimensional biomedical data are not explainable to humans.
method ALPODS method classifies data based on clusters and generates fuzzy reasoning rules.
result ALPODS provides understandable explanations for AI diagnoses.
StrokeSave app diagnoses stroke via mobile sensors and AI.
problem Inadequate stroke diagnosis in areas without quality medical facilities.
method Mobile app with sensors, facial and retinal images, deep learning model.
result Deep learning model achieves 95% accuracy in diagnosing stroke.
AI4COVID-19 app diagnoses COVID-19 from cough samples.
problem Scalable screening tool for COVID-19 testing.
method Transfer learning and multi-pronged AI architecture.
result AI4COVID-19 can distinguish COVID-19 coughs from others.
AI-assisted heart disease diagnosis reduces misdiagnosis and saves lives.
problem Misdiagnosis of heart disease leads to unnecessary deaths.
method Developed an AI application using ML and DNN algorithms on a dataset from the Cleveland Clinic Foundation.
result DNN model achieved a 92% accuracy rate, reducing misdiagnosis.
Novel method diagnoses large language models' reasoning abilities.
problem Fine-grained evaluation of large language models' reasoning abilities.
method Adapting cognitive diagnosis models to LLMs, estimating mastery profiles and Q-matrix, incorporating textual information.
result Accurate parameter recovery and insights into LLMs' capabilities.
Deep learning improves sleep apnea diagnosis accuracy.
problem Manual sleep expert scoring is tedious, time-consuming, and variable.
method Adapted deep learning method DOSED for automatic sleep event detection in PSG.
result Automatic approach achieved 81% accuracy for sleep apnea severity diagnosis.
New method accounts for uncertainty in medical AI evaluations.
problem Uncertainty in ground truth affects AI model performance estimates.
method Statistical aggregation approach to infer probabilities of medical conditions.
result Performance estimates are significantly lower when uncertainty is accounted for.
Experiment shows cognitive biases impact human-AI collaboration, highlighting the need for diverse evaluator samples.
problem Cognitive biases affect human-AI collaboration, leading to suboptimal outcomes.
method Randomized experiment with 2,784 participants, manipulating AI suggestion quality, task burden, and financial incentives.
result Individual attitudes toward AI are the strongest predictor of performance, influencing accuracy and overcorrection.
AI detects oral pre-cancerous lesions with high accuracy.
problem Manual screening of oral cavity cancer is expensive and lacks specialists.
method Deep convolutional neural networks (DCNNs) using transfer learning.
result DCNN models achieve high accuracy in distinguishing between benign and pre-cancerous tongue lesions.
Zero-Shot Learning helps learn new concepts without examples, useful for COVID-19 diagnosis.
problem Learning new concepts without examples, especially in medical imaging.
method Uses existing knowledge and auxiliary information to predict unknown concepts.
result Effective in diagnosing COVID-19 from chest X-rays.
Deep learning ensemble improves Alzheimer vs. Mild Cognitive Impairment diagnosis.
problem Differentiating Alzheimer Disease from Mild Cognitive Impairment.
method Hybrid deep learning ensemble framework using MRI slices, pretrained models, and stacked ensemble learning.
result State-of-the-art accuracy (99.21%) for Alzheimer vs. Mild Cognitive Impairment classification.
AI virtual doctor predicts diabetes from non-invasive data.
problem Limited access to primary medical care in rural areas.
method Interactive AI system with speech recognition and synthesis, deep neural networks.
result System accurately predicts type 2 diabetes.
AI enhances personalized drug development and decision-making in pharma.
problem Traditional drug development lacks personalized treatment plans.
method Application of AI in drug discovery, clinical trials, and post-marketing assessment.
result AI improves personalized medicine, optimizing health outcomes.
Online symptom checkers have significant potential to improve patient care, however their reliability and accuracy remain variable. We hypothesised that an artificial intelligence (AI) powered triage and diagnostic system would compare favourably with human doctors with respect to triage and diagnostic accuracy. We per…
End-to-end CAD system for thyroid nodule classification using multimodal data and expert guidance.
problem Improving accuracy in thyroid nodule classification for clinicians.
method Knowledge-driven DenseNet framework using multimodal ultrasound data and expert cues.
result The proposed system achieves relevant performances in thyroid nodule classification.
Paper uses AI to improve medical diagnosis accuracy.
problem Improving accuracy of medical diagnoses.
method Heuristic frequentist and Bayesian approaches applied to a nationwide dataset.
result Algorithm outperforms human doctors in detecting abnormal births.
SML improves pancreatic mass diagnosis accuracy using CT images.
problem Improving accuracy in pancreatic mass screening using CT imaging.
method Spectral machine learning method trained on 30,000 images, choosing fundamental images based on eigenvectors and removing irrelevant pixels.
result Achieved 94.6% test accuracy in diagnosing 113 patients.
Algorithmic insurance tackles financial risks from AI errors, proving CVaR-optimal thresholds reduce tail risk.
problem High-stakes AI errors lead to heterogeneous losses, challenging traditional insurance assumptions.
method Analyzed binary classification performance to tail risk exposure, using CVaR to quantify extreme losses.
result CVaR-optimal thresholds reduce tail risk up to 13-fold compared to accuracy maximization.
Bayesian CNN estimates uncertainty in COVID-19 detection.
problem Uncertainty in deep learning predictions for medical diagnosis.
method Drop-weight based Bayesian Convolutional Neural Networks (BCNN).
result Uncertainty correlates with prediction accuracy.
Paper analyzes uncertainty metrics in ensemble learning for healthcare AI.
problem Selecting appropriate uncertainty metrics for ensemble learners in healthcare AI.
method Rigorous analysis of two uncertainty metrics: ensemble mean and variance.
result Ensemble mean is preferable to ensemble variance for decision making in healthcare AI.
AI models forget statistics' lesson: correlation doesn't imply causation.
problem AI models often produce flawed causal models due to ignoring correlation vs causation.
method Demonstrates examples of flawed AI models and proposes rethinking core models.
result Current efforts to make AI models ethical are insufficient.
Method interprets deep learning models using topological data analysis.
problem Lack of interpretability in deep learning models, especially in high-risk applications.
method Topological and geometric data analysis to infer features and decision-making mechanisms of DL models.
result Extracted subgraphs reveal relevant features for model decisions, demonstrating model's reliance on pertinent data.
Study benchmarks uncertainty quantification in chest X-ray classification.
problem Reliable uncertainty quantification for medical AI models.
method Evaluation of 13 uncertainty quantification methods on MIMIC-CXR-JPG dataset.
result Insights into effectiveness and disentanglement of epistemic and aleatoric uncertainties.
Loss estimator improves model calibration and generalization.
problem Ensuring models generalize to unseen data.
method Train a loss estimator alongside the model using contrastive training.
result Improves model generalization and calibration.
Deep learning model improves X-ray disease detection accuracy in Thai patients.
problem Lack of large-scale validation of AI algorithms for medical image diagnosis in Thailand.
method Development and testing of a deep learning algorithm using 421,859 local chest radiographs.
result Convolutional neural networks achieve remarkable performance in detecting 13 common abnormality conditions on chest X-ray.
Pipeline detects pulmonary embolisms from sparse CT images.
problem Manual diagnosis of pulmonary embolisms is laborious and prone to errors.
method Two-stage pipeline using AI, sparse annotations, and robust models.
result Achieved AUC scores of 0.94 on validation and 0.85 on test sets for severe PEs.
X-Caps improves medical diagnosis explainability by encoding visual attributes in capsules.
problem Uninterpretable predictions from deep neural networks in healthcare.
method Teaches a novel multi-task capsule network to encode high-level visual attributes and malignancy scores.
result X-Caps outperforms state-of-the-art deep dense 3D CNNs in capturing visually interpretable attributes and malignancy prediction.
A new L2D system produces calibrated probabilities of expert correctness without sacrificing accuracy.
problem Calibration of learning to defer systems for safety.
method One-vs-all classifiers with a consistent surrogate loss function.
result Proposes a calibrated L2D system that outperforms existing methods in accuracy and calibration.
Study improves conformal prediction for EEG classification in healthcare, enhancing coverage.
problem Uncertainty quantification in clinical predictions, especially in distribution-shifted settings.
method Personalized calibration strategies to improve coverage of prediction sets.
result Coverage improved by over 20 percentage points with comparable prediction set sizes.
Deep learning diagnoses rotary machine faults without expert input.
problem Early detection of faults in rotary machinery to save time and money.
method Deep Convolutional Neural Network with three axis accelerometer signal input.
result High classification accuracy in fault diagnosis.
Quantum computing improves fault diagnosis in industrial processes.
problem Fault detection and diagnosis in industrial process systems.
method Integrates quantum computing and deep learning to extract features and diagnose faults.
result Quantum-assisted deep learning achieves high fault detection rates (79.2% and 99.39%).
Deep learning system diagnoses AVNFH from plain radiographs.
problem Challenging AVNFH diagnosis from plain radiographs.
method Deep convolutional neural networks for end-to-end diagnosis.
result AVN-net achieves state-of-the-art AUC of 0.97 in AVNFH detection.
Domain-Adversarial Neural Networks improve fault diagnosis models across different machines.
problem Improving fault diagnosis models on new machines with limited labeled data.
method Domain-Adversarial Neural Networks (DANN) and other methods for domain adaptation.
result Unified experimental protocol for fair comparison of domain adaptation methods.
This paper improves deep learning classifiers by integrating conformal prediction during training.
problem High-stake AI applications require reliable uncertainty estimates for safe deployment.
method Integrates conformal prediction (CP) during training of deep learning models.
result Reduces inefficiency and allows more control over confidence sets.
Paper presents adversarial method for explaining IDS misclassifications.
problem Limited interpretability of machine learning models in IDSs.
method Adversarial machine learning to generate feature modifications.
result Satisfactory explanations match expert knowledge.
Proposes TPIS for early and low-cost TB vs. pneumonia diagnosis.
problem Challenges in differentiating TB from pneumonia.
method Two-step decision support system with stacked ensemble classifiers.
result TPIS outperforms other methods in early and final diagnosis.
Deep neural networks (DNNs) have shown huge superiority over humans in image recognition, speech processing, autonomous vehicles and medical diagnosis. However, recent studies indicate that DNNs are vulnerable to adversarial examples (AEs), which are designed by attackers to fool deep learning models. Different from re…
Develops a data-driven fault diagnosis framework for time-series data.
problem Fault diagnosis of dynamic systems using imbalanced and unknown fault classes.
method Kullback-Leibler divergence, data-driven fault classification, open-set classification.
result Framework handles imbalanced datasets, class overlapping, and unknown faults.
A method uses ITD and XGBoost for precise power transformer fault diagnosis.
problem Fault diagnosis of power transformers using DGA data.
method Ranking DGA parameters by skewness, extracting ITD features, and using an XGBoost classifier.
result The method achieves over 95% accuracy in classification.
Novel hybrid modeling combines ML and physics for real-time diagnosis.
problem Real-time diagnosis of complex systems.
method Combines machine learning and physics-based models to create reduced-order models.
result Generated models are two orders of magnitude simpler, improving efficiency.
DiagNet uses adversarial learning and signed graph regularization for better mammography diagnosis.
problem Inadequate data and similarity between benign and cancerous masses in mammography.
method Adversarial learning to generate positive and negative mammograms, signed similarity graph, deep convolutional neural network training.
result DiagNet outperforms state-of-the-art in breast mass diagnosis.
Deep neural networks justify medical diagnoses with textual explanations.
problem Improving machine learning in medical diagnosis justification.
method Mapping X-Ray images to textual representations, generating explanations, and multi-task training.
result The method significantly outperforms existing justification methods and achieves high accuracy.
Counterfactual diagnosis improves medical accuracy and safety.
problem Existing diagnostic algorithms struggle with distinguishing correlation from causation.
method Reformulated diagnosis as a counterfactual inference task and derived new counterfactual diagnostic algorithms.
result Counterfactual diagnostic algorithms significantly improve accuracy and safety compared to standard Bayesian algorithms.
WD-DTL uses Wasserstein distance to transfer deep features for fault diagnosis.
problem Transfer learning difficulty in diverse working conditions with insufficient labelled data.
method Adversarial training with Wasserstein distance to align feature distributions.
result WD-DTL improves fault diagnosis accuracy in diverse conditions.
This paper studies the trade-off between model accuracy and coverage for diagnosis models used by patients.
problem Balancing accuracy and coverage in diagnosis models for patient use.
method Learned diagnosis models with varying coverage from EHR data.
result A 1% drop in top-3 accuracy for every 10 diseases added to the coverage.