MD tree diagnoses model failures using loss landscape metrics.
problem Diagnose model failures without knowing training configuration.
method MD tree based on loss landscape metrics.
result MD tree achieves 87.7% accuracy in dataset transfer tasks, outperforming validation-based approaches.
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…
Paper uses NLP to cluster patient visits for diagnosis validation.
problem Validating if similar patients receive similar diagnoses.
method Representation of medical visits using word embeddings, clustering patients' visits.
result Stable and separated segments of visits positively validated against diagnoses.
Machine learning model diagnoses COVID-19 from routine blood tests.
problem Difficulty in diagnosing COVID-19 due to inconsistent blood parameter changes.
method Constructed a machine learning model using 5,333 patients with various infections and 160 COVID-19-positive patients.
result Cross-validated AUC of 0.97, sensitivity of 81.9%, specificity of 97.9%.
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.
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.
This work develops a high precision fault diagnosis classifier using XAI insights.
problem Fault diagnosis of steel plates in manufacturing industry.
method Incorporation of XAI insights into Data Science process using SMOTE and medoids.
result 10 fold cross validated performance of 94% achieved.
AMI-Net+ tackles medical diagnosis from incomplete, imbalanced data.
problem Medical diagnosis from incomplete and imbalanced data.
method AMI-Net+ uses multi-instance neural network with embedding, multi-head attention, and gated attention-based pooling. It also employs focal loss and self-adaptive multi-instance pooling.
result AMI-Net+ outperforms state-of-the-art models on real-world medical datasets.
Radiomics approach improves cardiac CVD diagnosis from cine-MRI.
problem Inaccurate expert visualization or clinical indices for CVD classification.
method Estimating radiomic features from cine-MRI, feature selection, advanced machine learning.
result Radiomics features correctly classified 100 cases of five cardiac classes.
Improved fault diagnosis for bearings using mRMR and transfer learning.
problem Challenges in forming large-scale annotated datasets for machine fault diagnosis.
method Combining mRMR with deep learning and transfer learning.
result Improved fault diagnostics performance in terms of accuracy and computational complexity.
Few-shot learning improves bearing fault diagnosis with limited data.
problem Challenges in collecting sufficient fault data for robust classifier training.
method Model-Agnostic Meta-Learning (MAML) for few-shot learning.
result Framework achieves up to 25% higher accuracy than Siamese network.
In recent years, an increasing popularity of deep learning model for intelligent condition monitoring and diagnosis as well as prognostics used for mechanical systems and structures has been observed. In the previous studies, however, a major assumption accepted by default, is that the training and testing data are tak…
Neural network improves breast cancer diagnosis with high accuracy.
problem Improving accuracy in breast cancer diagnosis.
method Higher-order probabilistic perceptron (HOPP) model.
result HOPP model achieves up to 97% accuracy in classifying breast cancer tumors.
Quickly adapts fault diagnosis models for industrial machines.
problem Fault diagnostic models trained for lab machines fail on industrial ones.
method Net2Net transformation followed by fine-tuning.
result Models can be quickly adapted for new operating conditions.
Backward Conformal Prediction offers flexible control over prediction set sizes while ensuring coverage guarantees.
problem Providing reliable prediction sets with controlled sizes in applications like medical diagnosis.
method Defines a rule that constrains prediction set sizes based on observed data, adapting coverage levels.
result Maintains computable coverage guarantees while ensuring interpretable, well-controlled prediction set sizes.
The paper proposes a new method for comparing logistic regression models across different populations.
problem Comparing logistic regression models across sub-populations can lead to misleading results.
method Develops a cascading set of equivalence tests for logistic regression models, addressing coding, predictions, and overall accuracy.
result Equivalence testing incentivizes accurate inference and avoids perverse incentives from significance tests.
Eye tracking measures ADHD-related working memory deficits.
problem Diagnosing ADHD in adults requires reliable measures of working memory capacity.
method Eye tracking technology and machine learning applied to a working memory task.
result Machine learning generated features unique to ADHD.
This paper proposes a novel fault diagnosis approach based on generative adversarial networks (GAN) for imbalanced industrial time series where normal samples are much larger than failure cases. We combine a well-designed feature extractor with GAN to help train the whole network. Aimed at obtaining data distribution a…
Proposes a framework to explain KS deterioration in credit risk models.
problem Inconsistent and ad hoc diagnosis of KS decline in credit risk models.
method Counterfactual diagnostic framework attributing KS decline to sampling variability, portfolio composition, covariate shift, and residual deterioration.
result The proposed approach provides more interpretable and governance-relevant explanations than threshold-based review alone.
CNNs accurately measure airways and vessels on CT images, improving lung disease diagnosis.
problem Accurately characterizing small pulmonary structures from CT images for disease diagnosis.
method Generative model combined with Convolutional Neural Regressor (CNR) for cross-sectional measurements.
result CNNs provide accurate measurements with physiological correlates.
With the increased affordability and availability of whole-genome sequencing, large-scale and high-throughput gene expression is widely used to characterize diseases, including cancers. However, establishing specificity in cancer diagnosis using gene expression data continues to pose challenges due to the high dimensio…
In recent years, a number of artificial intelligent services have been developed such as defect detection system or diagnosis system for customer services. Unfortunately, the core in these services is a black-box in which human cannot understand the underlying decision making logic, even though the inspection of the lo…
Proposes a statistical test for VAE-based anomaly detection reliability.
problem Ensuring reliability of anomaly detection in high-stakes applications.
method Variance Autoencoder (VAE) Test based on selective inference.
result Validates VAE-based anomaly detection with p-values controlling false detection probability.
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.
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.
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.
DeTraC CNN detects COVID-19 from chest X-rays with high accuracy.
problem Limited availability of annotated medical images for COVID-19 diagnosis.
method Transfer learning and class decomposition mechanism in DeTraC CNN.
result 95.12% accuracy in detecting COVID-19 from normal and severe cases.
Ankle sprains and instability are major public health concerns. Up to 70% of individuals do not fully recover from a single ankle sprain and eventually develop chronic ankle instability (CAI). The diagnosis of CAI has been mainly based on self-report rather than objective biomechanical measures. The goal of this study …
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.
Machine learning models trained on indirect data labels can fail on real-world examples.
problem Validity issues in machine learning when target labels are indirectly defined.
method Identification of problematic datasets and models using a general procedure.
result Machine learning models trained on indirect data labels will fail on real-world examples.
Financial LLMs need explicit bias consideration to avoid invalid results.
problem Finance-specific biases inflate performance and contaminate backtests.
method Identified five recurring biases and proposed a Structural Validity Framework.
result Explicit bias consideration is necessary for valid deployment claims.
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 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.
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.
CPTD improves prediction intervals in time series regression with cross-sectional data.
problem Constructing valid prediction intervals in time series regression with a cross-section.
method Conformal Prediction with Temporal Dependence (CPTD) for post-hoc, light-weight approach.
result CPTD maintains cross-sectional validity while improving longitudinal coverage.
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.
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%.
Study on diagnosing unseen medical conditions using open-set learning.
problem Training models for unseen medical conditions is impractical.
method Frame diagnosis as an open-set learning problem, compare state-of-the-art approaches, and experiment with distributed training data.
result Explicitly modeling unseen conditions leads to consistent gains, but optimal training strategy varies.
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.
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.
A new method uses active learning to improve bile duct stone evaluation.
problem Efficiently collecting necessary patient data in sequential healthcare decisions.
method Developed an active learning-based multistage sequential decision-making model.
result Improves estimation efficiency by 62%-1838% compared to baseline methods.