Adaptive decision-making for state estimation with partial observations.
problem Stochastic state estimation with partial observations and active diagnosis.
method Weak adaptive submodularity and adaptive greedy policy.
result Adaptive greedy policy achieves near-optimal performance for weakly adaptive submodular reward functions.
The problem of active diagnosis arises in several applications such as disease diagnosis, and fault diagnosis in computer networks, where the goal is to rapidly identify the binary states of a set of objects (e.g., faulty or working) by sequentially selecting, and observing, (noisy) responses to binary valued queries. …
Inpatient2Vec learns representations for inpatients with multi-layer self-attention.
problem Lack of specialized RL methods for inpatient data with strong temporal relations and consistent diagnoses.
method Inpatient2Vec uses a multi-layer self-attention mechanism with two training tasks to learn medical activity, hospital day, and diagnosis representations for inpatients.
result Inpatient2Vec outperforms baselines on semantic similarity and clinical events prediction tasks.
Optimal observation selection for complex hidden hypotheses.
problem Diagnosis of complex hidden hypotheses using limited observations.
method Active diagnosis through selection of most informative observations based on past results.
result An implication model that predicts future outcomes based on past observations, selecting the most informative next observation.
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 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.
Deep belief network improves smartphone activity recognition.
problem Activity recognition on mobile devices.
method Categorization through deep belief network.
result 98.25% correct diagnosis in training data, 93.01% in test data.
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%.
A GAN-based method diagnoses faults in imbalanced industrial time series data.
problem Fault diagnosis in imbalanced industrial time series data.
method Generative adversarial networks (GAN) combined with a feature extractor.
result Our approach achieves excellent performance in detecting faults.
Deep neural network predicts ECG abnormalities from short-duration exams.
problem Improving accuracy of ECG diagnosis from short-duration exams.
method Residual neural network with 9 convolutional layers trained on large dataset.
result Model outperformed medical doctors on ECG abnormalities.
Combines deep learning with active learning for image data.
problem Challenges of active learning with deep learning models.
method Bayesian convolutional neural networks integrated into active learning framework.
result Significant improvement in active learning approaches for image data.
In this paper, we consider active information acquisition when the prediction model is meant to be applied on a targeted subset of the population. The goal is to label a pre-specified fraction of customers in the target or test set by iteratively querying for information from the non-target or training set. The number …
Issue found in proof of adaptive submodular function minimization theorem.
problem Proof of adaptive submodular function minimization theorem is incorrect.
method Example provided to show incorrectness of a critical step in a related theorem.
result Critical step in related theorem is found to be incorrect.
Develops visual explanations for Alzheimer's disease classification using 3D-CNNs.
problem Improving understanding of Alzheimer's disease classification using 3D-CNNs.
method Three approaches: sensitivity analysis and two activation visualization methods.
result Visual explanations identify important brain parts for Alzheimer's disease diagnosis.
Deep learning skin lesion classifier explained using CAVs.
problem Limited acceptance of deep learning CAD systems due to opaque decision-making.
method Mapped human understandable concepts to RECOD model using CAVs.
result Classifier learns and encodes disease-related concepts in its latent representation.
Combining brain structure and function for ASD diagnosis.
problem Identifying neuropathological bases of Autism Spectrum Disorder.
method Modeling brain structure as a graph, using rs-fMRI signals, and applying Graph Signal Processing.
result Decision tree outperforms state-of-the-art methods in diagnosing ASD.
TS-Insight visualizes Thompson Sampling for better debugging and trust.
problem Thompson Sampling's black box nature hinders debugging and trust.
method TS-Insight is a visual analytics tool that traces evolving posteriors and evidence counts.
result Visualizations help in verifying, diagnosing, and explaining Thompson Sampling dynamics.
Digital RNN improves dysgraphia detection in handwriting tests.
problem Early detection and remediation of handwriting difficulties.
method Recurrent Neural Network (RNN) model using a graphics tablet.
result RNN diagnoses dysgraphia with over 90% accuracy.
Active learning improves medical image segmentation by selecting optimal samples.
problem Limited manual annotations in medical datasets.
method Maximizing information at network abstraction layer and using Borda-count for sample selection.
result Improved segmentation performance with active learning.
Improved cGANs using GOLD measure for better data distribution alignment.
problem Improving the quality and controllability of cGANs.
method Measuring the discrepancy between data and model distributions using GOLD.
result Proposed GOLD measure improves cGANs in training, inference, and data selection.
CNNs help diagnose diabetic retinopathy by localizing lesions.
problem Diabetic retinopathy diagnosis requires identifying lesions in fundus images.
method Post-attention technique (Grad-CAM) on deep learning models' penultimate layer.
result InceptionV3 model achieves best performance and localizes lesions better.
Estimates cost savings from early cancer diagnosis.
problem Improving early cancer diagnosis to reduce treatment costs.
method Combining published cancer treatment cost estimates by stage with incidence rates by stage at diagnosis, and extrapolating to other cancer sites.
result Estimates U.S. national annual treatment cost-savings from early cancer diagnosis in the trillions.
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.
Improved Schizophrenia diagnosis using brain signal features with limited observations.
problem Ambulatory diagnoses of neuronal diseases with limited brain signal data.
method Pairwise distance learning approach using Siamese neural network and cosine contrastive loss.
result Improved accuracy and sensitivity in Schizophrenia diagnosis (+10pp).
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.
Deep neural network classifies DaTscan SPECT images for Parkinson's Disease.
problem Early diagnosis of Parkinson's Disease through objective analysis of SPECT images.
method InceptionV3 architecture with custom binary classifier, 10-fold cross validation.
result Deep neural network achieves high accuracy in classifying DaTscan SPECT images.
Wavelet-based CFC improves EEG seizure classification.
problem Improving accuracy in distinguishing ictal seizures from normal brain activity.
method Wavelet-based cross frequency coupling (CFC) for feature extraction, followed by t-test and QDA for classification.
result Wavelet-based CFC enhances classification accuracy of epileptic EEG signals.
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.
Study uses deep learning to detect BCCs in high-res histopathological images.
problem Detecting BCCs in high-resolution, weakly labeled histopathological images.
method Attention-based deep learning models to process ultra-high resolution images with weak labels.
result Attention-based models achieve almost perfect classification performance (AUC of 0.99).
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.
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.
Mask-RCNN applied to ISIC 2018 lesion tasks.
problem Lesion boundary segmentation, attributes detection, and diagnosis.
method Mask-RCNN applied to ISIC 2018 challenge tasks with a trained model for task 1 and a simple voting procedure for task 3.
result Improved lesion diagnosis accuracy using Mask-RCNN.
Model improves diagnosis from incomplete lab test data.
problem Incomplete and longitudinal lab test data challenges diagnosis accuracy.
method Joint training of generative VRNN and discriminative NN models.
result VRNN+NN significantly outperforms baseline models in diagnosis accuracy.
Automated MRI image quality assessment framework using machine learning.
problem Manual quality assessment of MRI images is time-consuming and costly.
method Machine learning model trained on human observer labels without reference images.
result Framework achieves 93.7% accuracy in estimating image quality.
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.
U-Det improves lung nodule segmentation in CT images.
problem Challenging shapes and surroundings of lung nodules in CT images.
method End-to-end deep learning with Bi-FPN, Mish activation, and class weights.
result U-Det achieves 82.82% Dice similarity coefficient, comparable to human experts.
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.
Study compares single vs ensemble feature selection for cancer diagnosis.
problem Identifying relevant variables for cancer diagnosis and prognosis.
method Comparison of single feature selection algorithms and ensemble of diverse algorithms.
result Ensemble approach did not improve predictive performance over individual algorithms.