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Processing multi-expert annotations in digital pathology: a study of the Gleason 2019 challenge 处理数字病理学中的多专家注释:对格里森2019年挑战的研究
Symposium on Medical Information Processing and Analysis Pub Date : 2021-12-10 DOI: 10.1117/12.2604307
Adrien Foucart, O. Debeir, C. Decaestecker
{"title":"Processing multi-expert annotations in digital pathology: a study of the Gleason 2019 challenge","authors":"Adrien Foucart, O. Debeir, C. Decaestecker","doi":"10.1117/12.2604307","DOIUrl":"https://doi.org/10.1117/12.2604307","url":null,"abstract":"Deep learning algorithms rely on large amounts of annotations for learning and testing. In digital pathology, a ground truth is rarely available, and many tasks show large inter-expert disagreement. Using the Gleason2019 dataset, we analyse how the choices we make in getting the ground truth from multiple experts may affect the results and the conclusions we could make from challenges and benchmarks. We show that using undocumented consensus methods, as is often done, reduces our ability to properly analyse challenge results. We also show that taking into account each expert’s annotations enriches discussions on results and is more in line with the clinical reality and complexity of the application.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125722652","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
Application of conditional GAN models in optic disc/optic cup segmentation of retinal fundus images 条件GAN模型在视网膜眼底图像视盘/视杯分割中的应用
Symposium on Medical Information Processing and Analysis Pub Date : 2021-12-10 DOI: 10.1117/12.2606209
Tales H. Carvalho, C. H. Moraes, R. C. Almeida, D. Spadoti
{"title":"Application of conditional GAN models in optic disc/optic cup segmentation of retinal fundus images","authors":"Tales H. Carvalho, C. H. Moraes, R. C. Almeida, D. Spadoti","doi":"10.1117/12.2606209","DOIUrl":"https://doi.org/10.1117/12.2606209","url":null,"abstract":"Analysis of retinal fundus images have been proven to provide relevant information about the diagnoses of several pathologies. Among them, glaucoma stands out as an important pathology due to the need for early treatment. Moreover, the relationship between optic disc and optic cup regions provided by retinal fundus image analysis can aid in diagnosis. Automatically generating such a relation is, therefore, an important feature for ensuring quicker and more precise conclusions. This paper evaluates the use of Conditional GAN (Generative Adversarial Networks) for an optic disc and optic cup segmentation task. Conditional GANs are hybrid machine learning models that are able to generate data based on conditioned training. The results demonstrate that the addressed method generates valid segmentation images for optic disc and optic cup location, with approximately 95% and 85% accuracy, respectively","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133592691","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: 1
A supervised subtype differentiation learning for building invariant features of non-small cell lung cancer in a latent space of a Variational Autoencoder 在变分自编码器的潜在空间中建立非小细胞肺癌的不变特征的监督亚型分化学习
Symposium on Medical Information Processing and Analysis Pub Date : 2021-12-10 DOI: 10.1117/12.2606255
F. Cano, Charlens Alvarez-Jimenez, D. Becerra, A. Siabatto, Angel Cruz-Roa, E. Romero
{"title":"A supervised subtype differentiation learning for building invariant features of non-small cell lung cancer in a latent space of a Variational Autoencoder","authors":"F. Cano, Charlens Alvarez-Jimenez, D. Becerra, A. Siabatto, Angel Cruz-Roa, E. Romero","doi":"10.1117/12.2606255","DOIUrl":"https://doi.org/10.1117/12.2606255","url":null,"abstract":"This work presents a novel quantification of the cancer extension using a latent space embedded metrics of a variational autoencoder which captures the invariant patterns of the disease and projects them into a smaller latent space where data relations are linear, making it possible to apply simple metrics to quantify complicated relations. Selected patches of non-small cell lung cancer are projected to such latent space and a logistic regression model assigns an Euclidean distance between the patches projected in space. A simple grouping strategy quantitatively stratifies the characteristic patterns of the most representative patches for both adenocarcinoma and squamous cell lung cancer classes but it also estimates the composition of a mixture of patterns. This approach is fully interpretable, integrable with a pathology work flow and an objective characterization of diseases with complex patterns.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"310 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133848652","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
A flexible AI pipeline for medical imaging in a radiology workflow 放射学工作流程中用于医学成像的灵活人工智能管道
Symposium on Medical Information Processing and Analysis Pub Date : 2021-12-10 DOI: 10.1117/12.2606146
Lucia Antunez, Julio Castellanos, G. Raposo, Camila Murga
{"title":"A flexible AI pipeline for medical imaging in a radiology workflow","authors":"Lucia Antunez, Julio Castellanos, G. Raposo, Camila Murga","doi":"10.1117/12.2606146","DOIUrl":"https://doi.org/10.1117/12.2606146","url":null,"abstract":"Medical imaging analysis is an effective technique and process for visualizing the human body’s interior to diagnose, monitor, and treat medical conditions. Artificial Intelligence (AI) brings new opportunities for improvement, with multiple applications in all levels of the radiology workflow. This paper presents a solution that leverages state-of-the-art models and architectures to assemble a modular pipeline for detection, segmentation, measurement, and scoring, that builds up to an optimized clinical report for medical imaging analysis and diagnosis. The proposed approach is designed to be flexible and tailor-made to an end facility’s needs and data, helping the radiologist’s effectiveness.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"571 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127084166","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
How cold waves influence LDL cholesterol levels? A regional study for Campinas, São Paulo, Brazil 寒潮如何影响低密度脂蛋白胆固醇水平?巴西圣保罗州坎皮纳斯的区域研究
Symposium on Medical Information Processing and Analysis Pub Date : 2021-12-10 DOI: 10.1117/12.2606220
T. R. Bella, J. P. de Lázari, W. Corozolla, D. S. de Oliveira, A. M. Heuminski de Ávila, E. D. de Faria, P. D. Paro Costa
{"title":"How cold waves influence LDL cholesterol levels? A regional study for Campinas, São Paulo, Brazil","authors":"T. R. Bella, J. P. de Lázari, W. Corozolla, D. S. de Oliveira, A. M. Heuminski de Ávila, E. D. de Faria, P. D. Paro Costa","doi":"10.1117/12.2606220","DOIUrl":"https://doi.org/10.1117/12.2606220","url":null,"abstract":"The increase in deaths from cardiovascular diseases in extreme temperature events, particularly during cold waves, is a phenomenon already reported in the literature. One of its mechanisms is the increased likelihood of atherosclerotic plaque formation due to higher concentrations of low-density lipoprotein (LDL-C) in the blood at periods of lower temperatures. This study adopts a data science approach to check evidences of this mechanism in the population of Campinas, a city in the southeast region of Brazil, with over 1 million inhabitants. We integrated climate and health datasets and processed over 1,677,424 LDL-C exam results in combination with minimum and maximum daily air temperature data in the city in eleven years (2008-2018). The data were stratified into sex and age groups, and we analyzed the difference in distributions of LDL-C levels for those exposed to cold waves versus control days. Cold waves were defined as at least three consecutive days with minimum and maximum temperatures below their 10th percentiles, considering a 30-year climate normal (1961- 1990). In particular, we analyzed the effect of cold waves on LDL-C levels above reference value. Our analyses identified nine cold waves in the period and statistically relevant effects on exam results. The amount LDL-C exams with levels above reference value was 3.32% greater for women between 20 and 65 years old (lag 2), 9.27%, 7.39% and 4.06% for women over 65 (lags 0, 2 and 7, respectively), and 11.45% for men over 65 (lag 4).","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128062310","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
Evolution of conditional-GANs for the synthesis of chest x-ray images 用于胸部x线图像合成的条件gan的进化
Symposium on Medical Information Processing and Analysis Pub Date : 2021-12-10 DOI: 10.1117/12.2606272
Juan-Antonio Rodríguez-de-la-Cruz, H. Acosta-Mesa, E. Mezura-Montes, F. Arámbula Cosío, B. Escalante-Ramírez, Jimena Olveres Montiel
{"title":"Evolution of conditional-GANs for the synthesis of chest x-ray images","authors":"Juan-Antonio Rodríguez-de-la-Cruz, H. Acosta-Mesa, E. Mezura-Montes, F. Arámbula Cosío, B. Escalante-Ramírez, Jimena Olveres Montiel","doi":"10.1117/12.2606272","DOIUrl":"https://doi.org/10.1117/12.2606272","url":null,"abstract":"Deep learning (DL) is now widely used to perform tasks involving the analysis of biomedical imaging. However, the small amounts available of annotated examples of these types of images make it difficult to use DL-based systems, since large amounts of data are required for adequate generalization and performance. For this reason, in recent years, Generative Adversarial Networks (GANs) have been used to obtain synthetic images that artificially increase the amount available. Despite this, the usual training instability in GANs, in addition to their empirical design, does not always allow for high-quality results. Through the neuroevolution of GANs it has been possible to reduce these problems, but many of these works use benchmark datasets with thousands of images, a scenario that does not reflect the real conditions of cases in which it is necessary to increase the data due to the limited amount available. In this work, cDCGAN-PSO is presented, an algorithm for the neuroevolution of GANs that adapts the concepts of the DCGAN-PSO to a conditional-DCGAN that allows the synthesis of three classes of chest X-ray images and that is trained with only 600 images of each class. The synthetic images obtained from evolved GANs show good similarity with real chest X-ray images.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129956217","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
Mining relations between neuropsychological data to characterize Alzheimer’s disease 挖掘神经心理学数据之间的关系以表征阿尔茨海默病
Symposium on Medical Information Processing and Analysis Pub Date : 2021-12-10 DOI: 10.1117/12.2606298
Germán A. Pabón, Diana L. Giraldo, E. Romero
{"title":"Mining relations between neuropsychological data to characterize Alzheimer’s disease","authors":"Germán A. Pabón, Diana L. Giraldo, E. Romero","doi":"10.1117/12.2606298","DOIUrl":"https://doi.org/10.1117/12.2606298","url":null,"abstract":"The quantitative characterization of Alzheimer’s Disease (AD) in early stages allows timely detection and prediction of disease progression. These are important to disease intervention and monitoring before clinical diagnosis of dementia. We used cognitive, functional and behavioral data from 612 Alzheimer’s Disease Neuroimaging Initiative (ADNI) individuals. First, we standardized, based on normative data, and dichotomized the selected variables. Grouping abnormal variables, we learned possible disease features from a sample of 225 cognitively impaired patients. Then, we quantify the manifestation for each disease feature and evaluated this quantitative characterization in the automated prediction of future progression from Mild Cognitive Impairment (MCI) to AD dementia. Five groups of abnormal neuropsychological measures were established describing five possible disease features. The resulting quantitative characterization for AD at prodromal stages predicts disease progression within the next 36 months with an accuracy of 0.76.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132109015","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
A non-contact SpO2 estimation using a video magnification technique 使用视频放大技术的非接触式SpO2估计
Symposium on Medical Information Processing and Analysis Pub Date : 2021-12-10 DOI: 10.1117/12.2606145
J. Brieva, E. Moya-Albor, Hiram Ponce
{"title":"A non-contact SpO2 estimation using a video magnification technique","authors":"J. Brieva, E. Moya-Albor, Hiram Ponce","doi":"10.1117/12.2606145","DOIUrl":"https://doi.org/10.1117/12.2606145","url":null,"abstract":"In this paper, we present a new non-contact strategy to estimate the Peripheral Oxygen Saturation (SPO2) based on the Eulerian motion video magnification technique and a signal processing technique, The magnification procedure was carried out using two approaches : the Hermite decomposition and the Gaussian decomposition. The SpO2 is estimated from the signals extracted after magnification process using the red and the blue Chanel of the frame. We have tested the method on five healthy subjects using videos obtained from the google-meet video conference platform. To compare the performance of the methods, we compute the mean average error and metrics issues from the Bland and Altman analysis to investigate the agreement of the methods with respect to a contact pulse oximeter device as reference. The proposed solution shows an agreement with respect to the reference of most of 98%","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127144381","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: 1
White matter connectivity alterations in patients with MRI negative temporal lobe epilepsy using hippocampus specific tractography MRI阴性颞叶癫痫患者的脑白质连通性改变
Symposium on Medical Information Processing and Analysis Pub Date : 2021-12-10 DOI: 10.1117/12.2607222
Yaqiong Chai, R. Cabeen, J. Simon, Yan Li, W. Muhlhofer, R. Knowlton, Hosung Kim
{"title":"White matter connectivity alterations in patients with MRI negative temporal lobe epilepsy using hippocampus specific tractography","authors":"Yaqiong Chai, R. Cabeen, J. Simon, Yan Li, W. Muhlhofer, R. Knowlton, Hosung Kim","doi":"10.1117/12.2607222","DOIUrl":"https://doi.org/10.1117/12.2607222","url":null,"abstract":"Temporal lobe epilepsy (TLE) patients with normal-appearing MRI scans on neuroradiological evaluation are very common (30% of all TLE), but pre-surgical evaluation is challenging for this TLE cohort, and as a result surgery does not often achieve a seizure-free outcome. The purpose of this study is to analyze diffusion magnetic resonance imaging (dMRI) by constructing tractography models that were seeded from the hippocampus to superficial white matter underlying the neocortex and subcortical grey matter structures. We compared mean fractional anisotropy (FA) values in 96 regions of interest between patients with unilateral TLE (10 left lateralized TLE, LTLE and 16 right lateralized TLE, RTLE) and 18 healthy controls. We found that both LTLE and RTLE showed significantly decreased FA values in hippocampal pathways to putamen, pallidum, parahippocampal and entorhinal cortex. In particular, LTLE patients mainly displayed unilateral FA decreases in hippocampal connections ipsilateral to the epileptic focus, whereas RTLE showed bilateral FA decreases. No connectivity changes were found between the hippocampus and the neocortical regions. Our analysis provides novel evidence of alterations in connectivity between the hippocampus and its proximal grey matter structures in patients with MRI-negative TLE.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126856516","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
Using deep image prior to assist variational selective segmentation deep learning algorithms 利用深度图像先验辅助变分选择性分割深度学习算法
Symposium on Medical Information Processing and Analysis Pub Date : 2021-12-01 DOI: 10.1117/12.2606212
Liam Burrows, Ke-long Chen, F. Torella
{"title":"Using deep image prior to assist variational selective segmentation deep learning algorithms","authors":"Liam Burrows, Ke-long Chen, F. Torella","doi":"10.1117/12.2606212","DOIUrl":"https://doi.org/10.1117/12.2606212","url":null,"abstract":"Variational segmentation algorithms require a prior imposed in the form of a regularisation term to enforce smoothness of the solution. Recently, it was shown in the Deep Image Prior work that the explicit regularisation in a model can be removed and replaced by the implicit regularisation captured by the architecture of a neural network. The Deep Image Prior approach is competitive, but is only tailored to one specific image and does not allow us to predict future images. We propose to incorporate the ideas from Deep Image Prior into a more traditional learning algorithm to allow us to use the implicit regularisation offered by the Deep Image Prior, but still be able to predict future images.","PeriodicalId":147201,"journal":{"name":"Symposium on Medical Information Processing and Analysis","volume":"595 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126901073","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
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