Search Results for author:
Found 14 papers, 6 papers with code
Clinical Domain Knowledge-Derived Template Improves Post Hoc AI Explanations in Pneumothorax Classification
We anticipate that our template guidance will forge a fresh approach to elucidating AI models by integrating clinical domain expertise.
Efficient scene text image super-resolution with semantic guidance
Scene text image super-resolution has significantly improved the accuracy of scene text recognition.
Foundation Model Makes Clustering A Better Initialization For Cold-Start Active Learning
In this work, we propose to integrate foundation models with clustering methods to select samples for cold-start active learning initialization.
Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation
We propose a novel approach that incorporates the lung+ space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs.
FedScore: A privacy-preserving framework for federated scoring system development
We also calculated the average AUC values and SDs for each local model, and the FedScore model showed promising accuracy and stability with a high average AUC value which was closest to the one of the pooled model and SD which was lower than that of most local models.
Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques
We found that model backbone(s) differed among data types as well as the imputation strategy.
Balanced background and explanation data are needed in explaining deep learning models with SHAP: An empirical study on clinical decision making
This study sought to investigate the effects of data imbalance on SHAP explanations for deep learning models, and to propose a strategy to mitigate these effects.
An empirical study of the effect of background data size on the stability of SHapley Additive exPlanations (SHAP) for deep learning models
In our empirical study on the MIMIC-III dataset, we show that the two core explanations - SHAP values and variable rankings fluctuate when using different background datasets acquired from random sampling, indicating that users cannot unquestioningly trust the one-shot interpretation from SHAP.
You Ought to Look Around: Precise, Large Span Action Detection
In this paper, we propose a method called YOLA (You Ought to Look Around) which includes three parts: 1) a robust backbone SPN-I3D for extracting spatio-temporal features.
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies
To some extent, current deep learning solutions can address these challenges.
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data
All scoring models were evaluated on the basis of their area under the curve (AUC) in the receiver operating characteristic analysis and balanced accuracy (i. e., mean value of sensitivity and specificity).
AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data
We illustrated our method in a real-life study of 90-day mortality of patients in intensive care units and compared its performance with survival models (i. e., Cox) and the random survival forest.
BIG-bench Machine Learning
Interpretable Machine Learning
+1
Elastic Deep Learning in Multi-Tenant GPU Clusters
We study how to support elasticity, that is, the ability to dynamically adjust the parallelism (i. e., the number of GPUs), for deep neural network (DNN) training in a GPU cluster.
A Framework for Recommending Accurate and Diverse ItemsUsing Bayesian Graph Convolutional Neural Networks
Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences.
