Search Results for author:
Found 12 papers, 4 papers with code
Dr.VAE: Drug Response Variational Autoencoder
We present two deep generative models based on Variational Autoencoders to improve the accuracy of drug response prediction.
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples
Many deep learning algorithms can be easily fooled with simple adversarial examples.
Reducing Adversarial Example Transferability Using Gradient Regularization
We investigate the transferability of adversarial examples between models using the angle between the input-output Jacobians of different models.
The importance of transparency and reproducibility in artificial intelligence research
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening.
Applications
CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image
In order to facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and parametric details in an open-source format.
Ranked #1 on
COVID-19 Diagnosis
on
Learning across label confidence distributions using Filtered Transfer Learning
In this work, we apply a transfer learning approach to improve predictive power in noisy data systems with large variable confidence datasets.
Deep-CR MTLR: a Multi-Modal Approach for Cancer Survival Prediction with Competing Risks
Accurate survival prediction is crucial for development of precision cancer medicine, creating the need for new sources of prognostic information.
A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data
We have conducted an institutional machine learning challenge to develop an accurate model for overall survival prediction in head and neck cancer using clinical data etxracted from electronic medical records and pre-treatment radiological images, as well as to evaluate the true added benefit of radiomics for head and neck cancer prognosis.
Lymph Node Graph Neural Networks for Cancer Metastasis Prediction
Predicting outcomes, such as survival or metastasis for individual cancer patients is a crucial component of precision oncology.
FastCPH: Efficient Survival Analysis for Neural Networks
The Cox proportional hazards model is a canonical method in survival analysis for prediction of the life expectancy of a patient given clinical or genetic covariates -- it is a linear model in its original form.
Maintaining Stability and Plasticity for Predictive Churn Reduction
Deployed machine learning models should be updated to take advantage of a larger sample size to improve performance, as more data is gathered over time.
Integrate Any Omics: Towards genome-wide data integration for patient stratification
High-throughput omics profiling advancements have greatly enhanced cancer patient stratification.
