Search Results for author:
Found 16 papers, 6 papers with code
Unsupervised Discovery of Clinical Disease Signatures Using Probabilistic Independence
Insufficiently precise diagnosis of clinical disease is likely responsible for many treatment failures, even for common conditions and treatments.
Why Do Probabilistic Clinical Models Fail To Transport Between Sites?
The rising popularity of artificial intelligence in healthcare is highlighting the problem that a computational model achieving super-human clinical performance at its training sites may perform substantially worse at new sites.
Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification
In this work, we propose a transformer-based multimodal strategy to integrate repeat imaging with longitudinal clinical signatures from routinely collected EHRs for SPN classification.
Sample-Specific Root Causal Inference with Latent Variables
Root causal analysis seeks to identify the set of initial perturbations that induce an unwanted outcome.
UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation
Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis.
Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography
In cross-validation on screening chest CTs from the NLST, our methods (0. 785 and 0. 786 AUC respectively) significantly outperform a cross-sectional approach (0. 734 AUC) and match the discriminative performance of the leading longitudinal medical imaging algorithm (0. 779 AUC) on benign versus malignant classification.
A Comparative Study of Confidence Calibration in Deep Learning: From Computer Vision to Medical Imaging
We believe that this study has merits to guide readers to choose calibration models and understand gaps between general computer vision and medical imaging domains.
Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Model
Complex diseases are caused by a multitude of factors that may differ between patients even within the same diagnostic category.
Identifying Patient-Specific Root Causes of Disease
Complex diseases are caused by a multitude of factors that may differ between patients.
Characterizing Renal Structures with 3D Block Aggregate Transformers
Efficiently quantifying renal structures can provide distinct spatial context and facilitate biomarker discovery for kidney morphology.
Generalizing Clinical Trials with Convex Hulls
This assumption allows us to extrapolate results from exclusive trials to the broader population by analyzing observational and trial data simultaneously using an algorithm called Optimum in Convex Hulls (OCH).
Lung Cancer Risk Estimation with Incomplete Data: A Joint Missing Imputation Perspective
To our knowledge, it is the first generative adversarial model that addresses multi-modal missing imputation by modeling the joint distribution of image and non-image data.
Synthesized Difference in Differences
We instead propose Synthesized Difference in Differences (SDD) that infers the correct (possibly non-parallel) slopes by linearly adjusting a conditional version of DD using additional RCT data.
The Value of Nullspace Tuning Using Partial Label Information
But in some learning problems, partial label information can be inferred from otherwise unlabeled examples and used to further improve the model.
Fully Automatic Liver Attenuation Estimation Combing CNN Segmentation and Morphological Operations
Manually tracing regions of interest (ROIs) within the liver is the de facto standard method for measuring liver attenuation on computed tomography (CT) in diagnosing nonalcoholic fatty liver disease (NAFLD).
Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data
Finally, we apply the method to clinical event data and demonstrate the face-validity of the abstraction, which is now amenable to standard learning algorithms.
