The Sample Complexity of Search over Multiple Populations

This paper studies the sample complexity of searching over multiple populations. We consider a large number of populations, each corresponding to either distribution P0 or P1. The goal of the search problem studied here is to find one population corresponding to distribution P1 with as few samples as possible. The main contribution is to quantify the number of samples needed to correctly find one such population. We consider two general approaches: non-adaptive sampling methods, which sample each population a predetermined number of times until a population following P1 is found, and adaptive sampling methods, which employ sequential sampling schemes for each population. We first derive a lower bound on the number of samples required by any sampling scheme. We then consider an adaptive procedure consisting of a series of sequential probability ratio tests, and show it comes within a constant factor of the lower bound. We give explicit expressions for this constant when samples of the populations follow Gaussian and Bernoulli distributions. An alternative adaptive scheme is discussed which does not require full knowledge of P1, and comes within a constant factor of the optimal scheme. For comparison, a lower bound on the sampling requirements of any non-adaptive scheme is presented.Comment: To appear, IEEE Transactions on Information Theor

Pancreatic cysts suspected to be branch duct intraductal papillary mucinous neoplasm without concerning features have low risk for development of pancreatic cancer.

BackgroundThe risk of developing pancreatic cancer is uncertain in patients with clinically suspected branch duct intraductal papillary mucinous neoplasm (BD-IPMN) based on the "high-risk stigmata" or "worrisome features" criteria proposed in the 2012 international consensus guidelines ("Fukuoka criteria").MethodsRetrospective case series involving patients referred for endoscopic ultrasound (EUS) of indeterminate pancreatic cysts with clinical and EUS features consistent with BD-IPMN. Rates of pancreatic cancer occurring at any location in the pancreas were compared between groups of patients with one or more Fukuoka criteria ("Highest-Risk Group", HRG) and those without these criteria ("Lowest-Risk Group", LRG).ResultsAfter exclusions, 661 patients comprised the final cohort (250 HRG and 411 LRG patients), 62% female with an average age of 67 years and 4 years of follow up. Pancreatic cancer, primarily adenocarcinoma, occurred in 60 patients (59 HRG, 1 LRG). Prevalent cancers diagnosed during EUS, immediate surgery, or first year of follow up were found in 48/661 (7.3%) of cohort and exclusively in HRG (33/77, 42.3%). Using Kaplan-Meier method, the cumulative incidence of cancer at 7 years was 28% in HRG and 1.2% in LRG patients (P<0.001).ConclusionsThis study supports using Fukuoka criteria to stratify the immediate and long-term risks of pancreatic cancer in presumptive BD-IPMN. The risk of pancreatic cancer was highest during the first year and occurred exclusively in those with "high-risk stigmata" or "worrisome features" criteria. After the first year all BD-IPMN continued to have a low but persistent cancer risk

Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms

Accurate semantic segmentation of each coronary artery using invasive coronary angiography (ICA) is important for stenosis assessment and coronary artery disease (CAD) diagnosis. In this paper, we propose a multi-step semantic segmentation algorithm based on analyzing arterial sements extraced from ICAs. The proposed algorithm firstly extracts the entire arterial binary mask (binary vascular tree) using a deep learning-based method. Then we extract the centerline of the binary vascular tree and separate it into different arterial segments. Finally, by extracting the underlyingarterial topology, position, and pixel features, we construct a powerful coronary artery segment classifier based on a support vector machine. Each arterial segment is classified into the left coronary artery (LCA), left anterior descending (LAD), and other types of arterial segments. The proposed method was tested on a dataset with 225 ICAs and achieved a mean accuracy of 70.33% for the multi-class artery classification and a mean intersection over union of 0.6868 for semantic segmentation of arteries. The experimental results show the effectiveness of the proposed algorithm, which provides impressive performance for analyzing the individual arteries in ICAs

Semantic Segmentation to Extract Coronary Arteries in Invasive Coronary Angiograms

Accurate semantic segmentation of each coronary artery using invasive coronary angiography (ICA) is important for stenosis assessment and coronary artery disease (CAD) diagnosis. In this paper, we propose a multi-step semantic segmentation algorithm based on analyzing arterial sements extraced from ICAs. The proposed algorithm firstly extracts the entire arterial binary mask (binary vascular tree) using a deep learning-based method. Then we extract the centerline of the binary vascular tree and separate it into different arterial segments. Finally, by extracting the underlyingarterial topology, position, and pixel features, we construct a powerful coronary artery segment classifier based on a support vector machine. Each arterial segment is classified into the left coronary artery (LCA), left anterior descending (LAD), and other types of arterial segments. The proposed method was tested on a dataset with 225 ICAs and achieved a mean accuracy of 70.33% for the multi-class artery classification and a mean intersection over union of 0.6868 for semantic segmentation of arteries. The experimental results show the effectiveness of the proposed algorithm, which provides impressive performance for analyzing the individual arteries in ICAs