Search Results for author:
Found 20 papers, 4 papers with code
Prior Knowledge Injection into Deep Learning Models Predicting Gene Expression from Whole Slide Images
We design the framework to be generic and flexibly adaptable to a wide range of architectures.
Identification of head impact locations, speeds, and force based on head kinematics
This study presents a deep learning model developed to accurately predict head impact information, including location, speed, orientation, and force, based on head kinematics during helmeted impacts.
Unraveling Radiomics Complexity: Strategies for Optimal Simplicity in Predictive Modeling
Purpose: Develop a methodology and tools to identify and explain the smallest set of predictive radiomic features.
Towards a more inductive world for drug repurposing approaches
In this work, we first perform an in-depth evaluation of current DTI datasets and prediction models through a robust benchmarking process, and show that DTI prediction methods based on transductive models lack generalization and lead to inflated performance when evaluated as previously done in the literature, hence not being suited for drug repurposing approaches.
Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects
Machine learning (ML) applications in medical artificial intelligence (AI) systems have shifted from traditional and statistical methods to increasing application of deep learning models.
Foundation Metrics for Evaluating Effectiveness of Healthcare Conversations Powered by Generative AI
The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare.
Reliability-based cleaning of noisy training labels with inductive conformal prediction in multi-modal biomedical data mining
This method capitalizes on a small set of accurately labeled training data and leverages ICP-calculated reliability metrics to rectify mislabeled data and outliers within vast quantities of noisy training data.
Toward more accurate and generalizable brain deformation estimators for traumatic brain injury detection with unsupervised domain adaptation
Machine learning head models (MLHMs) are developed to estimate brain deformation for early detection of traumatic brain injury (TBI).
Denoising instrumented mouthguard measurements of head impact kinematics with a convolutional neural network
Wearable sensors for measuring head kinematics can be noisy due to imperfect interfaces with the body.
Disparities in Dermatology AI Performance on a Diverse, Curated Clinical Image Set
To ascertain potential biases in algorithm performance in this context, we curated the Diverse Dermatology Images (DDI) dataset-the first publicly available, expertly curated, and pathologically confirmed image dataset with diverse skin tones.
Filter Drug-induced Liver Injury Literature with Natural Language Processing and Ensemble Learning
Drug-induced liver injury (DILI) describes the adverse effects of drugs that damage liver.
Disparities in Dermatology AI: Assessments Using Diverse Clinical Images
AI diagnostic tools may aid in early skin cancer detection; however most models have not been assessed on images of diverse skin tones or uncommon diseases.
Data-driven decomposition of brain dynamics with principal component analysis in different types of head impacts
The brain dynamics decomposition enables better interpretation of the patterns in brain injury metrics and the sensitivity of brain injury metrics across impact types.
Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion
To address the computational cost of FEM, the limited strain rate prediction, and the generalizability of MLHMs to on-field datasets, we propose data fusion and transfer learning to develop a series of MLHMs to predict the maximum principal strain (MPS) and maximum principal strain rate (MPSR).
Kinematics clustering enables head impact subtyping for better traumatic brain injury prediction
However, due to different kinematic characteristics, many brain injury risk estimation models are not generalizable across the variety of impacts that humans may sustain.
Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics
A random forest classifier with spectral densities of linear acceleration and angular velocity was built to classify head impact types (e. g., football, car crash, mixed martial arts).
Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation
To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in 1) the derivative order (angular velocity, angular acceleration, angular jerk), 2) the direction and 3) the power (e. g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events.
Relationship between brain injury criteria and brain strain across different types of head impacts can be different
The results show a significant difference in the relationship between BIC and brain strain across datasets, indicating the same BIC value may suggest different brain strain in different head impact types.
Deep Learning Head Model for Real-time Estimation of Entire Brain Deformation in Concussion
Results: The proposed deep learning head model can calculate the maximum principal strain for every element in the entire brain in less than 0. 001s (with an average root mean squared error of 0. 025, and with a standard deviation of 0. 002 over twenty repeats with random data partition and model initialization).
3-D Convolutional Neural Networks for Glioblastoma Segmentation
In this paper, we propose a framework of 3-D fully CNN models for Glioblastoma segmentation from multi-modality MRI data.
