Method learns feature maps from deep CNN layers for weakly supervised chest pathology localization.
problem Localization of chest pathologies in X-ray images is challenging due to varying sizes and appearances.
method Class-aware deep multiscale feature learning using intermediate feature maps from CNN layers.
result Improves localization performance of small pathologies like nodules and masses.
This paper characterizes VAE training pathologies and their effects on tasks.
problem Characterizing VAE training pathologies and their impact on downstream tasks.
method Concretely characterizing conditions for VAE training pathologies and their connection to specific downstream tasks.
result Connects VAE training pathologies to specific downstream tasks like learning compressed and disentangled representations, adversarial robustness, and semi-supervised learning.
SAPSAM trains CNNs on lung CTs with binary labels, improving CPA detection and localization.
problem Chronic Pulmonary Aspergillosis (CPA) detection and localization on CT scans using binary labels.
method Binary labels, average intensity projections, 2D RGB-like images, hierarchical CNN architectures.
result High classification accuracy, precise localization, predictive power of 2-year survival.
The paper identifies conditions for trend reversal in classification tasks.
problem Trend reversal in classification scores and dataset values.
method Algebraic conditions and numerical results for ridge regression.
result Existence of pathological regularization regimes for certain dataset conditions.
This study investigates how much knowledge from natural images can be transferred to pathology images.
problem Quantifying how much knowledge from natural images can be transferred to pathology images.
method Proposes a framework to quantify knowledge gain by a particular layer, conducts empirical investigation in pathology image centered transfer learning.
result Early layers of deep models can transfer knowledge to pathology image classification tasks.
KL-regularized RL from expert demos can lead to slow, unstable learning.
problem Pathological training dynamics in KL-regularized RL from expert demonstrations.
method Empirical analysis and non-parametric behavioral reference policies.
result KL-regularized RL can be significantly improved by using non-parametric behavioral policies.
Semi-supervised learning classifies cardiac pathology using motion features from cine MRI.
problem Classifying cardiac pathology based on motion features from cine MRI.
method Semi-supervised learning of apparent flow to generate motion features from non-segmented images.
result The model achieves 95% classification accuracy on ACDC test set.
Method treats pseudo healthy synthesis as a factor decomposition problem.
problem Creating a healthy-looking image from a pathological one.
method Adversarial training with paired or unpaired settings, combining two factors (healthy and disease) to reconstruct the input.
result Method outperforms conditional GAN and CycleGAN in generating pseudo healthy images.
Bayesian optimization improves chemical design by avoiding invalid molecules.
problem Bayesian optimization over variational autoencoder latent space produces invalid molecular structures.
method Formulated constrained Bayesian optimization to avoid querying far from training data.
result Marked improvements in validity of generated molecules.
Study improves voice disorder detection system robust to channel effects.
problem Voice signals are sensitive to recording devices.
method Bidirectional LSTM network with domain adversarial training (DAT).
result Increased PR-AUC from 0.8448 to 0.9455 (and 0.9522 with labels).
Generative model for morphological continuum of normal and pathological states.
problem Identifying trends and features that separate normality and pathology in biomedical images.
method Wasserstein Auto-encoder with HSIC regularization for latent features.
result Model generates a continuum of morphological changes corresponding to side information.
Approach for training deep nets with unlabeled patches.
problem Training deep neural networks with detailed expert annotations.
method Cluster-based learning from weakly labeled bags in latent space.
result Improved performance on Camelyon dataset.
Optimal prototypes found for challenging pathological geometries.
problem Finding optimal prototypes for pathological geometries is challenging.
method Analytical and heuristic algorithms for finding nearly-optimal prototypes.
result Optimal prototypes can be found analytically for challenging geometries.
Semi-supervised learning chain boosts histology image accuracy with minimal labeled data.
problem Expensive annotation of large histology image databases.
method Teacher-student knowledge distillation chain approach.
result Accuracy matches 100% labeled data with only 0.5% labeled data.
ConvNets improve EEG pathology detection accuracy.
problem Improving automated EEG diagnosis accuracy.
method Two ConvNet architectures, shallow and deep, trained on EEG data.
result ConvNets achieved ~6% better accuracy than previous methods.
Study classifies pathology reports using TF-IDF features and machine learning.
problem Classifying pathology reports for cancer surveillance and diagnostic workflow.
method Extracted TF-IDF features from pathology reports and classified them using SVM, XGBoost, and Logistic Regression.
result XGBoost achieved 92% accuracy in classifying pathology reports.
This paper analyzes GAN training and proposes Kernel GANs to improve performance.
problem Challenges in training Generative Adversarial Networks (GANs).
method Smoothed Jensen-Shannon Divergence (JSD) using kernel density estimates.
result Kernel GANs effectively improve GAN training on large-scale datasets.
Research aims to make AI decisions in digital pathology more understandable to pathologists.
problem Making AI decisions in digital pathology more understandable and interpretable.
method Combining machine learning with human intelligence to balance AI and human capabilities.
result Combining AI and human intelligence to improve diagnostic accuracy and understanding.
Proposes deep learning method for GCI detection from pathological speech.
problem Detecting glottal closure instants (GCI) in pathological acoustic speech.
method Convolutional neural network with fused deep acoustic speech and linear prediction residual features.
result Significantly better than state-of-the-art methods in GCI detection.
Generative model boosts bone lesion classification with augmented data.
problem Class imbalance and limited training data for bone lesion classification.
method Cycle-consistent GAN for image patch translation across bones.
result Generative augmentation improves classifier performance on held-out test set.
New strategies help CNNs recognize tissue features across different stains.
problem Training deep learning models for images with multiple stains is challenging and expensive.
method Presented unsupervised training strategies that leverage one staining modality to improve performance on images with multiple stains.
result CNNs trained with these strategies outperform standard training methods on images with multiple stains.
Generative model generates synthetic medical images for data augmentation and anonymization.
problem Imbalanced medical imaging data sets, especially for rare pathologies.
method Generative adversarial network (GAN) trained on two public brain MRI datasets.
result Synthetic images improve tumor segmentation performance and serve as an anonymization tool.
Deep learning models trained on adult cardiac MRI data struggle to accurately segment rare congenital heart diseases.
problem Accuracy of U-Net-based segmentation models trained on adult cardiac MRI data when applied to rare congenital heart diseases like Tetralogy of Fallot.
method Cross-validation with four-fold, evaluation on unseen data from different pathologies.
result Deep learning models overfit to the training data, leading to significant accuracy drops when applied to other pathologies.
Physics-informed neural networks struggle with stiffness leading to gradient imbalance.
problem Gradient pathologies in physics-informed neural networks.
method Learning rate annealing and novel neural network architecture.
result Significant improvement in predictive accuracy (50-100x) across various physics problems.
Novel approach characterizes deep neural networks at initialization.
problem Characterizing the behavior of deep neural networks at initialization.
method A novel approach considering the evolution of statistical moments of signal and noise.
result Established that skip-connections in residual networks lead to well-behaved moments and no pathology.
Batch normalization in the last layer reduces sharpness in wide neural networks.
problem Pathological sharpness in wide neural networks.
method Quantifying the geometry of the parameter space using Fisher information matrix and analyzing deep neural networks with random initialization.
result Batch normalization in the last layer significantly decreases pathological sharpness under specific conditions.
This work examines robust MCMC for pathological distributions.
problem Pathological behavior in target distributions affects MCMC efficiency.
method Reviewing and proposing remedies for roughness and flatness in MCMC.
result Robust MCMC algorithms can perform well even in challenging conditions.
Deep learning model predicts CXR findings from patient scans.
problem Insufficient radiologists for accurate CXR interpretation.
method Tagged 959k studies, trained deep learning model on 2M reports.
result Model performs similarly to radiologists on 12 findings.
Rotation-equivariant CNNs improve tumor detection in pathology.
problem Improving tumor detection in digital pathology images.
method Rotation-equivariant CNNs leveraging image symmetry.
result Significant improvement in tumor detection performance.
Paper develops a BERT-based classifier to reduce pathology report annotation workload.
problem Manual annotation of pathology reports is labor-intensive and time-consuming.
method Developed an automatic text classifier using BERT and introduced a human-centric metric to identify low-confidence cases.
result The model reduces manual annotation workload by 80% to 98%.
The paper presents algorithms for diagnosing Pathological Myopia and detecting retinal structures.
problem Diagnosing Pathological Myopia and detecting retinal structures in fundus images.
method The approach uses Deep Learning techniques, including transfer learning with Xception and YOLO architecture.
result The method has shown satisfactory results in the Pathologic Myopia Challenge.
New analysis explains pathology of deep Gaussian processes.
problem Pathology of deep Gaussian processes reduces learning capacities with increased layers.
method Study nonlinear dynamic systems corresponding to DGPs, derive recurrence relations.
result Provide tighter bounds and rate of convergence for dynamic systems.
Machine learning improves EEG pathology classification.
problem Automating clinical EEG analysis using machine learning.
method Developed a comprehensive feature-based framework and compared it to deep neural networks.
result Feature-based framework achieves accuracies similar to deep neural networks.
Model learns cancer tissue images onto a low-dimensional space revealing tissue characteristics.
problem Improving cancer diagnosis through high-fidelity digital pathology.
method Deep generative model using PathologyGAN to map real images onto a latent space.
result Latent space encodes morphological characteristics and reveals distinct tissue clusters.
Causal methods for GRN inference from single-cell data often fail in real-world benchmarks.
problem Understanding when and why causal methods for GRN inference from single-cell data fail in real-world benchmarks.
method Introduced a controlled diagnostic framework to isolate and measure seven pathologies.
result Causal methods dominate in clean and structurally favorable regimes but fail in specific pathologies.
A tool simplifies neural network training for medical image analysis.
problem Difficulties in training neural networks for medical image analysis.
method Intuitive interface for WSI annotation and display, human-in-the-loop strategy.
result Improved network performance through iterative annotation.
Researchers fix issues with variational Bayesian dropout.
problem Variational Bayesian dropout's theoretical framework has issues.
method Proposed new approximate inference objective called Quasi-KL (QKL).
result QKL addresses singularity issue and leads to Principal Component Analysis solution.
Regularized Stein thinning improves MCMC output approximations.
problem Pathologies in Stein thinning leading to poor approximations.
method Theoretical analysis and regularization to improve KSD.
result Regularized Stein thinning alleviates pathologies and improves efficiency.
Deep Learning model diagnoses four lymphoma categories with high accuracy.
problem Automated detection of lymphoma categories using digital pathology images.
method Convolutional neural network algorithm trained on 128 cases of lymph node images.
result Excellent diagnostic accuracy (95% image-by-image, 10% set-by-set).
For test configurations, the Donaldson-Futaki invariant F_1 is well-known. In this note, its refinement will be discussed. Then we see that Li-Xu's pathology doesn't occur, since their example of a non-normal test configuration, with trivial normalization, actually has non-vanishing F_1 in this refined sense.
New test identifies specific biological parameters for personalized CVD detection.
problem Ineffectual pathology tests fail to consider platelet activation and inter-individual variability.
method Stochastic platelet deposition model and approximate Bayesian computation with discriminative summary statistics.
result Inferred parameters help identify specific biological parameters for personalized CVD detection.
This paper proposes synthetic augmentation for nuclei image segmentation in medical pathology.
problem Rare and time-consuming labeling of tumor nuclei images for semantic segmentation.
method Label-to-image translation to generate synthetic images.
result Synthetic augmentation improves segmentation accuracy.
This paper improves sampling from complex distributions using Langevin dynamics.
problem Pathological behaviors in normalizing flows for complex distributions.
method A Metropolis adjusted Langevin algorithm (MALA) to sample in the latent space.
result The method preserves tractability of the likelihood and works with any pre-trained NF network.
DECAT framework evaluates multimodal models for shared biology, detecting confounders and false positives.
problem Determining if multimodal models learn shared biology or just confounders.
method DECAT framework classifies multimodal representations into four diagnostic scenarios using null-referenced metrics.
result DECAT detects confounders and false positives in multimodal models, improving with larger cohorts and stronger representations.
Study reveals pathological eigenvalue spectra in FIM and its variants of DNNs.
problem Understanding sharp local shapes in DNN loss landscapes.
method Analysis of FIM and its variants in regression and classification DNNs.
result Pathological eigenvalue spectra appear in FIM and its variants, indicating sharp local shapes in specific directions.
PSGD accelerates RNN training, achieving competitive performance.
problem Training recurrent neural networks, especially those with long-term memory requirements.
method Preconditioned stochastic gradient descent (PSGD) algorithm.
result PSGD achieves highly competitive performance on RNN training tasks.
Information divergence functions play a critical role in statistics and information theory. In this paper we show that a non-parametric f-divergence measure can be used to provide improved bounds on the minimum binary classification probability of error for the case when the training and test data are drawn from the sa…
New neural network accelerates cancer research analysis.
problem Slow inference speeds limit cancer research analysis.
method Automatically generated, tailored neural network architecture.
result 16x faster inference on cancer pathology data.