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.
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.
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.
Polysomnography (PSG) is the gold standard for diagnosing sleep obstructive apnea (OSA). It allows monitoring of breathing events throughout the night. The detection of these events is usually done by trained sleep experts. However, this task is tedious, highly time-consuming and subject to important inter-scorer varia…
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.
Background: Parkinson's disease (PD) is a prevalent long-term neurodegenerative disease. Though the diagnostic criteria of PD are relatively well defined, the current medical imaging diagnostic procedures are expertise-demanding, and thus call for a higher-integrated AI-based diagnostic algorithm. Methods: In this pape…
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.
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 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.
Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. However, these systems are widely used, e.g., in diabetes or cancer pr…
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.
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.
Recent trends focusing on Industry 4.0 concept and smart manufacturing arise a data-driven fault diagnosis as key topic in condition-based maintenance. Fault diagnosis is considered as an essential task in rotary machinery since possibility of an early detection and diagnosis of the faulty condition can save both time …
As the first diagnostic imaging modality of avascular necrosis of the femoral head (AVNFH), accurately staging AVNFH from a plain radiograph is critical yet challenging for orthopedists. Thus, we propose a deep learning-based AVNFH diagnosis system (AVN-net). The proposed AVN-net reads plain radiographs of the pelvis, …
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.
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.
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.
Quantum computing (QC) and deep learning techniques have attracted widespread attention in the recent years. This paper proposes QC-based deep learning methods for fault diagnosis that exploit their unique capabilities to overcome the computational challenges faced by conventional data-driven approaches performed on cl…
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.
Thanks to digitization of industrial assets in fleets, the ambitious goal of transferring fault diagnosis models fromone machine to the other has raised great interest. Solving these domain adaptive transfer learning tasks has the potential to save large efforts on manually labeling data and modifying models for new ma…
Discovering oral cavity cancer (OCC) at an early stage is an effective way to increase patient survival rate. However, current initial screening process is done manually and is expensive for the average individual, especially in developing countries worldwide. This problem is further compounded due to the lack of speci…
We estimate treatment cost-savings from early cancer diagnosis. For breast, lung, prostate and colorectal cancers and melanoma, which account for more than 50% of new incidences projected in 2017, we combine published cancer treatment cost estimates by stage with incidence rates by stage at diagnosis. We extrapolate to…
Machine learning aids in diagnosing Parkinson's disease with higher accuracy.
problem Subjectivity in traditional PD diagnosis methods and missed early symptoms.
method Machine learning applied to various data modalities for PD and control group classification.
result Machine learning methods show high potential for improving PD diagnosis.
Optimizes test set size for accurate diagnosis using machine learning.
problem Determining the minimum test set size for accurate diagnosis.
method Proposes machine learning methods (LASSO and SVM) to predict optimal test set size.
result SVM achieves 90.4% accuracy with a reduced test set by 35.24%.
New definition of patient-specific root causes of disease using counterfactuals.
problem Lack of rigorous mathematical formulation for automatic detection of root causes.
method Proposes a counterfactual definition matching clinical intuition and uses Shapley values for causal contribution scores.
result Adapts to disease prevalence, accounts for noisy labels, and admits fast computation.
An integrated approach is proposed across visual and textual data to both determine and justify a medical diagnosis by a neural network. As deep learning techniques improve, interest grows to apply them in medical applications. To enable a transition to workflows in a medical context that are aided by machine learning,…
Residual generation helps diagnose engine faults using neural networks.
problem Fault diagnosis in engines with unknown classes and limited data.
method Grey-box recurrent neural networks incorporating physical insights.
result Improved fault classification and root cause identification.
LPF provides formal guarantees for aggregating multi-evidence in probabilistic tasks.
problem Lack of formal guarantees for multi-evidence reasoning in AI.
method LPF uses variational autoencoders and Sum-Product Networks to aggregate evidence items.
result Proves multiple formal guarantees including calibration preservation and error decay.
Pulmonary embolisms (PE) are known to be one of the leading causes for cardiac-related mortality. Due to inherent variabilities in how PE manifests and the cumbersome nature of manual diagnosis, there is growing interest in leveraging AI tools for detecting PE. In this paper, we build a two-stage detection pipeline tha…
Machine learning for ASD diagnosis using morphological MRI networks.
problem Challenging to diagnose ASD using MRI due to heterogeneity and incomplete network neuroscience.
method Crowdsourced Kaggle competition to develop and benchmark ML pipelines.
result First-ranked team achieved 70% accuracy, 72.5% sensitivity, and 67.5% specificity.
The development of machine learning systems for the diagnosis of rare diseases is challenging mainly due the lack of data to study them. Despite this challenge, this paper proposes a system for the Computer Aided Diagnosis (CAD) of low-prevalence, congenital muscular dystrophies from confocal microscopy images. The pro…