arXiv subjects

Chris Carter

Publications and source records attributed to Chris Carter.

4 recordsLinked to original sources

arXiv2021

OncoPetNet: A Deep Learning based AI system for mitotic figure counting on H&E stained whole slide digital images in a large veterinary diagnostic lab setting

Background: Histopathology is an important modality for the diagnosis and management of many diseases in modern healthcare, and plays a critical role in cancer care. Pathology samples can be large and require multi-site sampling, leading to upwards of 20 slides for a single tumor, and the human-expert tasks of site selection and and quantitative assessment of mitotic figures are time consuming and subjective. Automating these tasks in the setting of a digital pathology service presents significant opportunities to improve workflow efficiency and augment human experts in practice. Approach: Multiple state-of-the-art deep learning techniques for histopathology image classification and mitotic figure detection were used in the development of OncoPetNet. Additionally, model-free approaches were used to increase speed and accuracy. The robust and scalable inference engine leverages Pytorch's performance optimizations as well as specifically developed speed up techniques in inference. Results: The proposed system, demonstrated significantly improved mitotic counting performance for 41 cancer cases across 14 cancer types compared to human expert baselines. In 21.9% of cases use of OncoPetNet led to change in tumor grading compared to human expert evaluation. In deployment, an effective 0.27 min/slide inference was achieved in a high throughput veterinary diagnostic pathology service across 2 centers processing 3,323 digital whole slide images daily. Conclusion: This work represents the first successful automated deployment of deep learning systems for real-time expert-level performance on important histopathology tasks at scale in a high volume clinical practice. The resulting impact outlines important considerations for model development, deployment, clinical decision making, and informs best practices for implementation of deep learning systems in digital histopathology practices.

eess.IV↗

arXiv2018

The Correlated Particle Hybrid Sampler for State Space Models

Particle Markov Chain Monte Carlo (PMCMC) is a general computational approach to Bayesian inference for general state space models. Our article scales up PMCMC in terms of the number of observations and parameters by generating the parameters that are highly correlated with the states \lq integrated out\rq{} in a pseudo marginal step; the rest of the parameters are generated conditional on the states. The novel contribution of our article is to make the pseudo-marginal step much more efficient by positively correlating the numerator and denominator in the Metropolis-Hastings acceptance probability. This is done in a novel way by expressing the target density of the PMCMC in terms of the basic uniform or normal random numbers used in the sequential Monte Carlo algorithm instead of the standard way in terms of state particles. We also show that the new sampler combines and generalizes two separate particle MCMC approaches: particle Gibbs and the correlated pseudo marginal Metropolis-Hastings. We investigate the performance of the hybrid sampler empirically by applying it to univariate and multivariate stochastic volatility models having both a large number of parameters and a large number of latent states and show that it is much more efficient than competing PMCMC methods.

stat.ME↗

arXiv2017

Efficient Bayesian inference for multivariate factor stochastic volatility models with leverage

This paper discusses the efficient Bayesian estimation of a multivariate factor stochastic volatility (Factor MSV) model with leverage. We propose a novel approach to construct the sampling schemes that converges to the posterior distribution of the latent volatilities and the parameters of interest of the Factor MSV model based on recent advances in Particle Markov chain Monte Carlo (PMCMC). As opposed to the approach of Chib et al. (2006} and Omori et al. (2007}, our approach does not require approximating the joint distribution of outcome and volatility innovations by a mixture of bivariate normal distributions. To sample the free elements of the loading matrix we employ the interweaving method used in Kastner et al. (2017} in the Particle Metropolis within Gibbs (PMwG) step. The proposed method is illustrated empirically using a simulated dataset and a sample of daily US stock returns.

stat.ME↗

arXiv2013

Multiple decision trees

This paper describes experiments, on two domains, to investigate the effect of averaging over predictions of multiple decision trees, instead of using a single tree. Other authors have pointed out theoretical and commonsense reasons for preferring the multiple tree approach. Ideally, we would like to consider predictions from all trees, weighted by their probability. However, there is a vast number of different trees, and it is difficult to estimate the probability of each tree. We sidestep the estimation problem by using a modified version of the ID3 algorithm to build good trees, and average over only these trees. Our results are encouraging. For each domain, we managed to produce a small number of good trees. We find that it is best to average across sets of trees with different structure; this usually gives better performance than any of the constituent trees, including the ID3 tree.

cs.LG↗