Symposium on Medical Information Processing and Analysis最新文献_第10页

A deep learning model for classification of diabetic retinopathy in eye fundus images based on retinal lesion detection 基于视网膜病变检测的眼底图像糖尿病视网膜病变深度学习分类模型

Symposium on Medical Information Processing and Analysis Pub Date : 2021-10-14 DOI: 10.1117/12.2606319

Melissa delaPava, Hern'an R'ios, Francisco J. Rodr'iguez, Oscar J. Perdomo, Fabio A. Gonz'alez

{"title":"A deep learning model for classification of diabetic retinopathy in eye fundus images based on retinal lesion detection","authors":"Melissa delaPava, Hern'an R'ios, Francisco J. Rodr'iguez, Oscar J. Perdomo, Fabio A. Gonz'alez","doi":"10.1117/12.2606319","DOIUrl":"https://doi.org/10.1117/12.2606319","url":null,"abstract":"Diabetic retinopathy (DR) is the result of a complication of diabetes affecting the retina. It can cause blindness, if left undiagnosed and untreated. An ophthalmologist performs the diagnosis by screening each patient and analyzing the retinal lesions via ocular imaging. In practice, such analysis is time-consuming and cumbersome to perform. This paper presents a model for automatic DR classification on eye fundus images. The approach identifies the main ocular lesions related to DR and subsequently diagnoses the illness. The proposed method follows the same workflow as the clinicians, providing information that can be interpreted clinically to support the prediction. A subset of the kaggle EyePACS and the Messidor-2 datasets, labeled with ocular lesions, is made publicly available. The kaggle EyePACS subset is used as training set and the Messidor-2 as a test set for lesions and DR classification models. For DR diagnosis, our model has an area-under-the-curve, sensitivity, and specificity of 0.948, 0.886, and 0.875, respectively, which competes with state-of-the-art approaches.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127655614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Diffusion MRI metrics and their relation to dementia severity: effects of harmonization approaches 弥散MRI指标及其与痴呆严重程度的关系:协调方法的效果

Symposium on Medical Information Processing and Analysis Pub Date : 2021-10-05 DOI: 10.1101/2021.10.04.21263994

S. Thomopoulos, T. Nir, J. Villalon-Reina, A. Zavaliangos-Petropulu, Piyush Maiti, Hong Zheng, Elnaz Nourollahimoghadam, N. Jahanshad, P. Thompson

{"title":"Diffusion MRI metrics and their relation to dementia severity: effects of harmonization approaches","authors":"S. Thomopoulos, T. Nir, J. Villalon-Reina, A. Zavaliangos-Petropulu, Piyush Maiti, Hong Zheng, Elnaz Nourollahimoghadam, N. Jahanshad, P. Thompson","doi":"10.1101/2021.10.04.21263994","DOIUrl":"https://doi.org/10.1101/2021.10.04.21263994","url":null,"abstract":"Diffusion-weighted magnetic resonance imaging (dMRI) is sensitive to microstructural changes in the brain that occur with normal aging and Alzheimer’s disease (AD). There is much interest in which dMRI measures are most strongly correlated with (1) AD diagnosis, (2) clinical measures of AD severity, such as the clinical dementia rating (CDR), and (3) biological processes that may be disrupted in AD, such as brain amyloid load measured using PET. Of these processes, some can be targeted using novel drugs. Since 2016, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) has collected dMRI data from three scanner manufacturers across 58 sites using 7 different protocols that vary in angular resolution, scan duration, and distribution of diffusion-weighted gradients. Here, we assessed dMRI data from 730 of those individuals (447 healthy controls, 214 with mild cognitive impairment, 69 with dementia; age: 74.1±7.9 years; 381 female/349 male). To harmonize data from different protocols, we applied ComBat, ComBat-GAM, and CovBat to dMRI metrics from 28 brain regions of interest. We ranked all dMRI metrics in order of the strength of clinically relevant associations, and assessed how this depended on the harmonization methods employed. dMRI metrics were strongly associated with age and AD severity, but also with amyloid positivity. All harmonization methods gave comparable results when assessing associations with age, dementia and amyloid load, while enabling data integration across multiple scanners and protocols.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130601828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Global and local interpretation of black-box machine learning models to determine prognostic factors from early COVID-19 data 从COVID-19早期数据确定预后因素的黑箱机器学习模型的全球和本地解释

Symposium on Medical Information Processing and Analysis Pub Date : 2021-09-10 DOI: 10.1117/12.2604743

Ananya Jana, Carlos D Minacapelli, V. Rustgi, Dimitris N. Metaxas

{"title":"Global and local interpretation of black-box machine learning models to determine prognostic factors from early COVID-19 data","authors":"Ananya Jana, Carlos D Minacapelli, V. Rustgi, Dimitris N. Metaxas","doi":"10.1117/12.2604743","DOIUrl":"https://doi.org/10.1117/12.2604743","url":null,"abstract":"The COVID-19 corona virus has claimed 4.1 million lives, as of July 24, 2021. A variety of machine learning models have been applied to related data to predict important factors such as the severity of the disease, infection rate and discover important prognostic factors. Often the usefulness of the findings from the use of these techniques is reduced due to lack of method interpretability. Some recent progress made on the interpretability of machine learning models has the potential to unravel more insights while using conventional machine learning models.1–3 In this work, we analyze COVID-19 blood work data with some of the popular machine learning models; then we employ state-of-the-art post-hoc local interpretability techniques(e.g.- SHAP, LIME), and global interpretability techniques(e.g. - symbolic metamodeling) to the trained black-box models to draw interpretable conclusions. In the gamut of machine learning algorithms, regressions remain one of the simplest and most explainable models with clear mathematical formulation. We explore one of the most recent techniques called symbolic metamodeling to find the mathematical expression of the machine learning models for COVID-19. We identify Acute Kidney Injury (AKI), initial Albumin level (ALB I), Aspartate aminotransferase (AST I), Total Bilirubin initial (TBILI) and D-Dimer initial (DIMER) as major prognostic factors of the disease severity. Our contributions are - (i) uncover the underlying mathematical expression for the black-box models on COVID-19 severity prediction task (ii) we are the first to apply symbolic metamodeling to this task, and (iii) discover important features and feature interactions. Code repository: https://github.com/ananyajana/interpretable covid19.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115516236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Secure neuroimaging analysis using federated learning with homomorphic encryption 使用同态加密的联邦学习安全神经成像分析

Symposium on Medical Information Processing and Analysis Pub Date : 2021-08-07 DOI: 10.1117/12.2606256

Dimitris Stripelis, Hamza Saleem, Tanmay Ghai, N. Dhinagar, Umang Gupta, Chrysovalantis Anastasiou, G. V. Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, J. Ambite

{"title":"Secure neuroimaging analysis using federated learning with homomorphic encryption","authors":"Dimitris Stripelis, Hamza Saleem, Tanmay Ghai, N. Dhinagar, Umang Gupta, Chrysovalantis Anastasiou, G. V. Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, J. Ambite","doi":"10.1117/12.2606256","DOIUrl":"https://doi.org/10.1117/12.2606256","url":null,"abstract":"Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to a centralized location. This results in an improved generalizability of models and efficient scaling of computation as more sources and larger datasets are added to the federation. Nevertheless, recent membership attacks show that private or sensitive personal data can sometimes be leaked or inferred when model parameters or summary statistics are shared with a central site, requiring improved security solutions. In this work, we propose a framework for secure FL using fullyhomomorphic encryption (FHE). Specifically, we use the CKKS construction, an approximate, floating point compatible scheme that benefits from ciphertext packing and rescaling. In our evaluation on large-scale brain MRI datasets, we use our proposed secure FL framework to train a deep learning model to predict a person’s age from distributed MRI scans, a common benchmarking task, and demonstrate that there is no degradation in the learning performance between the encrypted and non-encrypted federated models.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"637 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116212259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 29

Automatic evaluation of human oocyte developmental potential from microscopy images 人卵母细胞发育潜力的显微图像自动评价

Symposium on Medical Information Processing and Analysis Pub Date : 2021-02-27 DOI: 10.1117/12.2604010

Denis Baručić, J. Kybic, O. Teplá, Zinovij Topurko, I. Kratochvílová

引用次数: 3

A novel approach for QRS complex detection in patients with atrial arrhythmia 心房心律失常QRS复合体检测的新方法

Symposium on Medical Information Processing and Analysis Pub Date : 2020-11-03 DOI: 10.1117/12.2576019

Jader Giraldo-Guzmán, E. Ruiz, L. A. Magre Colorado, Gloria Isabel Bautista Lasprilla, M. Kotas

{"title":"A novel approach for QRS complex detection in patients with atrial arrhythmia","authors":"Jader Giraldo-Guzmán, E. Ruiz, L. A. Magre Colorado, Gloria Isabel Bautista Lasprilla, M. Kotas","doi":"10.1117/12.2576019","DOIUrl":"https://doi.org/10.1117/12.2576019","url":null,"abstract":"Atrial fibrillation (AF) is the most common cardiac arrhythmia in the world which shows rising prevalence leading to increased comorbidities, such as, Ischemic heart disease and Stroke that the main cause of deaths in the world. Since AF and most of the arrhythmias are generated due to electrical problems at the heart, electrocardiography provides the best noninvasive method to diagnose and QRS complex play an important role as a benchmark. In this paper, a novel methodology for QRS complex detection is presented. The algorithm introduces a modification of the well known Pan Tompkins approach, performing a multi channel detection, based on the signal to noise ratio of every channel. After application of the squaring operation in the channels with the highest signal to noise ratio a new single channel is created with improved quality, allowing the accurate detection of the QRS complexes in signals with atrial arrhythmias. The approach was tested in electrocardiography records from the Hospital Universitario de Valencia in Spain, showing an average positive predictive value of 99.6% and an average sensitivity of 99.9%.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116494873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep transfer learning of brain shape morphometry predicts Body Mass Index (BMI) in the UK Biobank 在英国生物银行，脑形态测量的深度迁移学习预测身体质量指数(BMI)

Symposium on Medical Information Processing and Analysis Pub Date : 2020-11-03 DOI: 10.1117/12.2577074

L. Zeng, C. Ching, Zvart Abaryan, S. Thomopoulos, Kai Gao, A. Zhu, A. Ragothaman, Faisal M. Rashid, Marc Harrison, Lauren E. Salminen, Brandalyn C. Riedel, N. Jahanshad, D. Hu, P. Thompson

{"title":"Deep transfer learning of brain shape morphometry predicts Body Mass Index (BMI) in the UK Biobank","authors":"L. Zeng, C. Ching, Zvart Abaryan, S. Thomopoulos, Kai Gao, A. Zhu, A. Ragothaman, Faisal M. Rashid, Marc Harrison, Lauren E. Salminen, Brandalyn C. Riedel, N. Jahanshad, D. Hu, P. Thompson","doi":"10.1117/12.2577074","DOIUrl":"https://doi.org/10.1117/12.2577074","url":null,"abstract":"Prior studies show that obesity is associated with accelerated brain aging and specific patterns of brain atrophy. Finerscale mapping of the effects of obesity on the brain would help to understand how it promotes or interacts with disease effects, but so far, the influence of the obesity on finer-scale maps of anatomy remains unclear. In this study, we propose a deep transfer learning network based on Optimal Mass Transport (OMTNet) to classify individuals with normal versus overweight/obese body mass index (BMI) using vertex-wise brain shape metrics extracted from structural MRI scans from the UK Biobank study. First, an area-preserving mapping was used to project 3D brain surface meshes onto 2D planar meshes. Vertex-wise maps of brain metrics such as cortical thickness were mapped into 2D planar images for each brain surface extracted from each person’s MRI scan. Second, several popular networks pretrained on the ImageNet database, i.e., VGG19, ResNet152 and DenseNet201, were used for transfer learning of brain shape metrics. We combined all shape metrics and generated a metric ensemble classification, and then combined all three networks and generated a network ensemble classification. The results reveal that transfer learning always outperforms direct learning, and we obtained accuracies of 65.6±0.7% and 62.7±0.7% for transfer and direct learning in the network ensemble classification, respectively. Moreover, surface area and cortical thickness, especially in the left hemisphere, consistently achieved the highest classification accuracies, together with subcortical shape metrics. The findings suggest a significant and classifiable influence of obesity on brain shape. Our proposed OMTNet method may offer a powerful transfer learning framework that can be extended to other vertex-wise brain structural and functional imaging measures.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116735607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Segmentation of retinal fluids and hyperreflective foci using deep learning approach in optical coherence tomography scans 在光学相干断层扫描中使用深度学习方法分割视网膜液体和高反射焦点

Symposium on Medical Information Processing and Analysis Pub Date : 2020-11-03 DOI: 10.1117/12.2579934

Yeison D. Sanchez, Bernardo Nieto, Fabio D. Padilla, Oscar J. Perdomo, F. G. González Osorio

{"title":"Segmentation of retinal fluids and hyperreflective foci using deep learning approach in optical coherence tomography scans","authors":"Yeison D. Sanchez, Bernardo Nieto, Fabio D. Padilla, Oscar J. Perdomo, F. G. González Osorio","doi":"10.1117/12.2579934","DOIUrl":"https://doi.org/10.1117/12.2579934","url":null,"abstract":"Retinal diseases are a common cause of blindness around the world, early detection of clinical findings can help to avoid vision loss in patients. Optical coherence tomography images have been widely used to diagnose retinal diseases, due to the capacity to show in detail findings as drusen, hyperreflective foci, and intraretinal and subretinal fluids. The location of findings is vital to identify and follow-up the retinal disease. However, the detection and segmentation of these findings is not an easy task due to artifacts noise, and the time consuming even to experts ophthalmologist. This paper proposes a computational method based on deep learning to automatically identify fluids and hyperreflective foci as a tool to identify retinal diseases through the use of OCT images. The method was evaluated on a set of OCT images manually annotated by experts. The experimental results present a Dice coefficient of 0,4437 and 0,6245 in the segmentation task of fluids (intrarretinal fluids and subretinal fluids), and hyperreflective foci respectively.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122601073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Endoscopic ultrasound database of the pancreas 胰腺的内镜超声数据库

Symposium on Medical Information Processing and Analysis Pub Date : 2020-11-03 DOI: 10.1117/12.2581321

María Jaramillo, Josué Ruano, Martín Gómez, E. Romero

{"title":"Endoscopic ultrasound database of the pancreas","authors":"María Jaramillo, Josué Ruano, Martín Gómez, E. Romero","doi":"10.1117/12.2581321","DOIUrl":"https://doi.org/10.1117/12.2581321","url":null,"abstract":"Pancreatic Cancer (PC) is a very aggressive cancer, with a mortality of 0.98 and a 5-year survival rate of 6.7%.1–3 Endoscopic ultrasonography (EUS) is the imaging modality to early detection of PC. Its reported diagnosis sensitivity for an experienced gastroenterologist ranges from 87 to 100%.3–5 Computational strategies, as Elastography, have been developed to support mass malignancy diagnosis. However, most studies evaluate their strategies using private datasets, making results incomparable. This work presents an annotated open access database of Endoscopy Ultrasound videos obtained in the Gastroenterology Unit of the Hospital Universitario Nacional de Colombia and the Unidad de Gastroenterolog´ıa y Ecoendoscopia. The dataset consists in a set of 55 cases acquired in B-mode Ultrasound image, composed of 18 cases with pancreatic cancer, 5 cases with pancreatitis, and 32 cases that include healthy pancreas, liver and gallbladder. Cases were confirmed and staged by pathological examination from biopsy samples and manually annotated per each video frame. Additionally, herein it is presented a preprocessing methodology aimed to highlight the useful echo patterns to differentiate pancreatic diseases.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124973691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Non-contact breathing rate monitoring system using a magnification technique and artificial hydrocarbon networks 采用放大技术和人工碳氢化合物网络的非接触式呼吸率监测系统

Symposium on Medical Information Processing and Analysis Pub Date : 2020-11-03 DOI: 10.1117/12.2580077

J. Brieva, Hiram Ponce, E. Moya-Albor

引用次数: 1