Tree model predicts breast cancer in older women.
problem Breast cancer prediction in older women with DCIS grades.
method Tree augmented naive Bayesian network trained on clinical data.
result Biopsy threshold recommendation of >2% for older women.
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48 results for “biopsy threshold”
CNN predicts kidney function from biopsies.
problem Predict future kidney function from biopsies.
method Used Convolutional Neural Networks (CNNs).
result CNNs accurately predict kidney function.
PersonalizedUS assesses breast cancer risk with local coverage guarantees.
problem Manual BI-RADS scoring leads to unnecessary biopsies and mental health burden.
method Conformal prediction for precise, personalized risk estimates.
result Local coverage guarantees with high sensitivity and specificity.
Convolutional neural networks improve biopsy image classification for diagnosing Celiac Disease and Environmental Enteropathy.
problem Diagnosing Celiac Disease and Environmental Enteropathy from biopsy images due to histopathologic overlap.
method Proposed a convolutional neural network (CNN) to classify duodenal biopsy images.
result The proposed model achieves high accuracy in classifying biopsy images for CD, EE, and healthy controls.
Generative models create H&E-stained and destained prostate biopsy images.
problem Lack of H&E-stained prostate biopsy images.
method Conditional GAN for H&E staining, destaining model learning from stained to non-stained images.
result Generated images maintain structural similarity to non-stained biopsy.
Study proposes deep learning techniques to diagnose and differentiate Celiac Disease and Environmental Enteropathy from biopsy images.
problem Challenging histopathologic overlap between Celiac Disease and Environmental Enteropathy in biopsy images.
method Color balancing and Random Multimodel Deep Learning (RMDL) architecture to address staining variability.
result Proposed deep learning techniques improve diagnosis accuracy of Celiac Disease and Environmental Enteropathy.
Deep learning model diagnoses celiac disease severity from intestinal biopsy images.
problem Diagnosing celiac disease severity from biopsy images, especially mild cases.
method Deep residual networks trained on a modified Marsh score histological scoring system.
result Model achieved AUC > 0.96 in all classes for CD severity classification.
Capsule network improves polyp diagnosis accuracy.
problem Low accuracy of optical biopsy methods for polyps.
method D-Caps architecture with CAP method.
result 43% improvement over previous state-of-the-art.
Mammography is the most effective and available tool for breast cancer screening. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. Data mining algorithms could be used to help physicians in their dec…
Deep learning system classifies breast cancer biopsy images with high accuracy.
problem Accurately differentiate between normal and cancerous breast tissue.
method Convolutional capsule network for four types of breast biopsy images.
result Cross-validation accuracy of 0.87 with high sensitivity.
Machine learning classifies colorectal tissue using photoacoustic microscopy.
problem Traditional diagnostic methods for colorectal cancer are limited in detail and painful.
method Machine learning applied to acoustic resolution photoacoustic microscopy.
result Machine learning accurately classified benign and malignant tissue.
A deep learning approach classifies medical images hierarchically.
problem Limitations of traditional supervised classifiers in medical image classification.
method Hierarchical Medical Image Classification (HMIC) using deep learning models.
result HMIC achieved better performance in classifying medical images hierarchically.
An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learningcs.CV
Paper reviews melanoma detection in dermoscopy images using image processing and machine learning.
problem Early detection and differentiation of melanoma from benign lesions.
method Lesion segmentation, clinical feature segmentation, machine learning algorithms.
result Machine learning algorithms can predict melanoma with high accuracy.
Deep object detection improves mitotic nucleus detection in breast cancer biopsies.
problem Challenges in automated mitotic nucleus detection in breast cancer histopathological images.
method Adapted Mask R-CNN for deep object detection, initially selects candidate regions with maximum recall, refines them with multi-object loss function.
result Improved discrimination ability (F-score of 0.86) and significant precision (0.86) for mitotic nuclei compared to two-stage models.
Hierarchical CNNs improve diagnosis of GI diseases from histopathological images.
problem Diagnosing GI diseases from histopathological images is challenging due to heterogeneity and shared features.
method Embedded a class hierarchy into a VGGNet to address the hierarchical structure of GI diseases.
result The hierarchical model achieved better results than a flat model for multi-category diagnosis of GI disorders.
Robust cancer screening model using pre-trained ensembles for biomarkers.
problem Detecting early-stage cancer, especially in hard-to-diagnose cases like pancreatic cancer.
method Meta-trained Hyperfast model for robust classification, combined with ensembling of XGBoost and LightGBM.
result Achieved highest AUC of 0.9929 and robust performance on imbalanced datasets.
Optimal iterative thresholding algorithms improve upon hard and soft thresholding.
problem Optimizing sparsity or rank constraints in optimization problems.
method Developed the notion of relative concavity for thresholding operators, finding a new class of operators that are optimal.
result A new class of thresholding operators, including thresholding and reciprocal thresholding, achieves the strongest convergence guarantee.
Develops CSI for predicting disease progression from sparse data.
problem Lack of frequent clinical metrics for disease progression.
method Machine learning framework (CSI) for analyzing disease progression from sparse observations.
result CSI effectively predicts disease progression from sparse data.
Developed a new thresholding method that connects soft and hard thresholding.
problem Connecting soft and hard thresholding methods in data analysis.
method Scaled soft thresholding method with empirical scaling values.
result Found two sources of over-fitting in the scaled soft thresholding method.
Paper proposes a method to identify optimal threshold for stock market networks.
problem Challenges in identifying the optimal threshold for reliable stock network construction.
method Dynamic consistence between threshold network and stock market, optimal threshold maximized by consistence function.
result Optimal threshold value of 0.28 for stocks in S&P 500 Index.
New method learns optimal nonlinearities for iterative thresholding algorithms.
problem Finding sparse solutions to ill-posed inverse problems.
method Relates ISTA iterations to DNN layers and develops error backpropagation for fine-tuning thresholding functions.
result Potential gains in estimation quality through data-adaptive ISTA.
New algorithm for reinforcement learning in uncertain environments with unknown thresholds.
problem Safety in reinforcement learning in unknown and uncertain environments.
method Growing-Window estimator sampling and Stochastic Pessimistic-Optimistic Thresholding (SPOT) algorithm.
result Achieves sublinear regret and constraint violation of .
A new SSL method uses instance-dependent thresholds to improve accuracy.
problem Improving semi-supervised learning by better selecting confident unlabeled instances.
method Proposes instance-dependent thresholds that vary based on the ambiguity and error rates of pseudo-labels for each unlabeled instance.
result Demonstrates that instance-dependent thresholds provide a probabilistic guarantee for correct pseudo-labels.
Investment timing problem solved with threshold strategies in real options theory.
problem Investment timing problem in real options theory.
method Modeling investment project present values as a diffusion process, proving conditions for optimal investment time and optimality of threshold strategies.
result Necessary and sufficient conditions for an optimal investment time induced by a threshold strategy.
Study stability thresholds of big line bundles, proving bounds and generalizing results.
problem Stability thresholds of big line bundles and their asymptotic behavior.
method Explicit bounds on error terms, using quasi-monomial valuations to compute stability thresholds.
result Proves Jin--Rubinstein--Tian's questions affirmatively.
Adaptive algorithm for outlier detection by balancing arm exploration and threshold estimation.
problem Identifying outliers in a set of rewards where the threshold is a function of all rewards.
method Adaptively updated confidence interval for the threshold based on previous rounds' estimates, balancing exploration of individual arms and the outlier threshold.
result Efficient algorithm with reduced sample complexity for outlier detection.
The Identification of Thresholds and Time Delay in Self-Exciting Threshold AR Model by Waveletmath-ph
In this paper we studied about the wavelet identification of the thresholds and time delay for more general case without the constraint that the time delay is smaller than the order of the model. Here we composed an empirical wavelet from the SETAR (Self-Exciting Threshold Autoregressive) model and identified the thres…
Proposes efficient data acquisition for personalized treatment effects from observational data.
problem Efficiently acquiring outcomes for personalized treatment effects in observational studies.
method Introduces causal, Bayesian acquisition functions to select points with overlapping support.
result Demonstrates improved sample efficiency and accuracy in learning personalized treatment effects.
The article examines different thresholding methods for improving PAM algorithm in cancer classification.
problem High-dimensional classification with too many features selected by PAM.
method Extends PAM with hard and order thresholding methods and a deep search algorithm.
result Improved cancer status prediction accuracy and smaller number of features.
In the work of Ammann, Dahl and Humbert it has turned out that the Yamabe invariant on closed manifolds is a bordism invariant below a certain threshold constant. A similar result holds for a spinorial analogon. These threshold constants are characterized through Yamabe-type equations on products of spheres with rescal…
Conservative Likelihood Ratio Estimator for Infrequent Data Slightly above a Frequency Thresholdstat.ML
Proposes a conservative LR estimator for infrequent data near a frequency threshold.
problem Overestimation of likelihood ratios for infrequent data near a frequency threshold.
method Conservative likelihood ratio estimator for frequencies slightly above a threshold.
result Improves prediction accuracy in named entity context prediction.
New method trains neural networks with threshold activation functions efficiently.
problem Training neural networks with threshold activation functions is challenging due to zero gradients.
method We study weight decay regularized training problems of deep neural networks with threshold activations, showing they can be formulated as convex optimization problems.
result Regularized deep threshold network training problems can be formulated as standard convex optimization problems, paralleling the LASSO method.
Optimizes threshold selection for variance estimation in financial models.
problem Estimating integrated variance in financial models with jumps.
method Optimizes threshold selection using mean and conditional mean square error criteria.
result Proposes a novel method to approximate the optimal threshold.
Noise makes learning linear thresholds hard, but algorithms can still learn near-optimal thresholds.
problem Learning linear thresholds in noisy data.
method Exploiting natural assumptions on data-generating process.
result Efficient learning of near-optimal linear thresholds is still possible with small data even in the presence of noise.
Study log canonical thresholds on curved metrics on group compactifications.
problem Computing log canonical thresholds for curved metrics on group compactifications.
method Associated convex functions to metrics, using asymptotic behavior to determine thresholds.
result Formula for alpha invariant in terms of polytope associated to group compactification.
Bayesian framework proves thresholds for multi-graph alignment feasibility.
problem Determining when multi-graph alignment is statistically possible.
method Developed a Bayesian estimation framework over metric spaces.
result Identified thresholds for Gaussian and sparse Erdős-Rényi models.
Paper develops DLTF to learn optimized dictionaries for efficient thresholded feature recovery.
problem Efficiently recover sparse code support from time-consuming sparse coding.
method Formulates DLTF model to learn optimized dictionary for thresholded feature, derives log-linear time proximal operator.
result DLTF model demonstrates remarkable efficiency, effectiveness, and robustness in various tasks.
Paper introduces threshold invariant fairness to ensure equitable predictions across different groups.
problem Machine learning models can be unfair to certain groups based on sensitive attributes.
method Proposes threshold invariant fairness and uses two approximation methods to equalize risk distributions.
result Demonstrates effectiveness in alleviating threshold sensitivity in fairness models.
Polynomial neural networks explore thresholds for maximum expressiveness.
problem Understanding the limits of polynomial neural networks' expressiveness.
method Introducing activation degree threshold to measure network expressiveness and proving its existence and upper bounds.
result Polynomial neural networks with equi-width architectures achieve the maximum expressiveness.
Study thresholds and K-stability using valuations in complex projective varieties.
problem Understanding thresholds and K-stability in complex projective varieties.
method Using valuations to study log canonical and stability thresholds, proving infima are attained for ample L, and obtaining simple expressions in toric cases.
result The thresholds can be written as infima of functionals on the space of valuations, and are attained for ample L.
Study evaluates thresholds for removing noise from DNN weights using random matrix theory.
problem Removing noise from deep neural network weights for better approximation.
method Model weights as signal + noise, use random matrix theory to estimate thresholds, evaluate using cosine similarity.
result Proposed threshold estimation method improves approximation quality.
Modeling firm default with a variable threshold based on management decisions.
problem Estimating default probability with asymmetric information.
method Generalized structural model with a variable default threshold.
result The information level significantly impacts default probability and credit yield spread.
FILTER model uses fusion penalized logistic threshold regression for high-dimensional data with unknown cut points.
problem Modeling high-dimensional data with unknown cut points and binary responses.
method Fusion penalized logistic threshold regression (FILTER) model with fused lasso penalty for variable selection.
result Established non-asymptotic error bounds for coefficient estimation and model selection consistency.
Optimal method detects jumps in jump-diffusion processes.
problem Detecting jumps in jump-diffusion processes with improved finite-sample performance.
method Iterative threshold-kernel method to optimally select threshold parameter.
result Approximate optimal threshold depends on spot volatility, jump intensity, and jump density.
LSA minimizes aggregate regret in thresholding bandit with optimal performance.
problem Finding arms with mean rewards above a threshold within a fixed budget.
method LSA, a simple and anytime algorithm aiming to minimize aggregate regret.
result LSA is instance-wise asymptotically optimal in minimizing aggregate regret.
Paper proves integrability and entropy compactness for Kähler potentials with uniform log-log threshold.
problem Integrability and entropy compactness for Kähler potentials with specific density.
method Skoda-Zeriahi type integrability theorem and log-log threshold detection.
result Positivity of integrability threshold and entropy compactness for uniform log-log threshold.
Optimizes waiting thresholds for node health states in Azure.
problem Minimizing downtime when nodes transition to unhealthy states.
method Customized organic recovery model using node features and gradient descent.
result Optimal thresholds can be found for multiple states in a state machine.
A new algorithm infers causal networks from data using topological thresholds.
problem Inferring causal networks from data.
method Two methods for determining topological thresholds: one to leave no disconnected nodes, the other to find a causal large connected component.
result The novel algorithm is faster and more accurate than the PC algorithm.