Pub Date : 2022-07-01DOI: 10.1109/CBMS55023.2022.00086
João Rafael Almeida, J. Barraca, J. L. Oliveira
One of the main goals of clinical studies consists of identifying diseases' causes and improving the efficacy of medical treatments. Sometimes, the reduced number of participants is a limiting factor for these studies, leading researchers to organise multi-centre studies. However, sharing health data raises certain concerns regarding patients' privacy, namely related to the robustness of anonymisation procedures. Although these techniques remove personal identifiers from registries, some studies have shown that anonymisation procedures can sometimes be reverted using specific patients' characteristics. In this paper, we propose a secure architecture to explore distributed databases without compromising the patient's privacy. The proposed architecture is based on interoperable repositories supported by a common data model.
{"title":"A secure architecture for exploring patient-level databases from distributed institutions","authors":"João Rafael Almeida, J. Barraca, J. L. Oliveira","doi":"10.1109/CBMS55023.2022.00086","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00086","url":null,"abstract":"One of the main goals of clinical studies consists of identifying diseases' causes and improving the efficacy of medical treatments. Sometimes, the reduced number of participants is a limiting factor for these studies, leading researchers to organise multi-centre studies. However, sharing health data raises certain concerns regarding patients' privacy, namely related to the robustness of anonymisation procedures. Although these techniques remove personal identifiers from registries, some studies have shown that anonymisation procedures can sometimes be reverted using specific patients' characteristics. In this paper, we propose a secure architecture to explore distributed databases without compromising the patient's privacy. The proposed architecture is based on interoperable repositories supported by a common data model.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122168617","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}
Pub Date : 2022-07-01DOI: 10.1109/CBMS55023.2022.00048
L. Lapp, M. Roper, K. Kavanagh, S. Schraag
Delirium, affecting up to 52% of cardiac surgery patients, can have serious long-term effects on patients by damaging cognitive ability and causing subsequent functional decline. This study reports on the development and evaluation of predictive models aimed at identifying the likely onset of delirium on an hourly basis in intensive care unit following cardiac surgery. Most models achieved a mean AUC > 0.900 across all lead times. A support vector machine achieved the highest performance across all lead times of AUC = 0.941 and Sensitivity = 0.907, and BARTm, where missing values were replaced with missForest imputation, achieved the highest Specificity of 0.892. Being able to predict delirium hours in advance gives clinicians the ability to intervene and optimize treatments for patients who are at risk and avert potentially serious and life-threatening consequences.
{"title":"Predicting the Onset of Delirium on Hourly Basis in an Intensive Care Unit Following Cardiac Surgery","authors":"L. Lapp, M. Roper, K. Kavanagh, S. Schraag","doi":"10.1109/CBMS55023.2022.00048","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00048","url":null,"abstract":"Delirium, affecting up to 52% of cardiac surgery patients, can have serious long-term effects on patients by damaging cognitive ability and causing subsequent functional decline. This study reports on the development and evaluation of predictive models aimed at identifying the likely onset of delirium on an hourly basis in intensive care unit following cardiac surgery. Most models achieved a mean AUC > 0.900 across all lead times. A support vector machine achieved the highest performance across all lead times of AUC = 0.941 and Sensitivity = 0.907, and BARTm, where missing values were replaced with missForest imputation, achieved the highest Specificity of 0.892. Being able to predict delirium hours in advance gives clinicians the ability to intervene and optimize treatments for patients who are at risk and avert potentially serious and life-threatening consequences.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"182 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121090054","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}
Pub Date : 2022-07-01DOI: 10.1109/CBMS55023.2022.00062
Sunyong Seo, S. Yoo, Semin Kim, Daeun Yoon, Jonghan Lee
Poresare minute skin openings through which hair and sebum come out and appear as holes in the facial skin. Enlarged pore is one of the major concerns for people who care about their skin. Remedies include the use of cosmetics and pore-reduction medical procedures. Awareness of the condition of one's facial pores and appropriate management are required to prevent pore deterioration. Pore segmentation algorithms based on classical image processing are characterized by low accuracy and high computational costs. In addition, these algorithms require that input images be taken in light-controlled environments. These issues were resolved by using a light-specialized data augmentation method and a neural network with a narrow receptive field for identifying local features. We introduce Pore-Net, an algorithm that can be used on mobile devices to segment pores with a low computational cost, using selfie-camera images as an input. Pore-Net has the following algorithm flow. First, a confidence map-based segmentation without encoder-decoder form is applied to lower the computational costs on high-resolution input images. Second, pre- and post-processing for input based on region-of-interest(ROI) of facial landmarks are performed to work robustly in mobile devices. Pore-Net achieved the lowest computational cost in inference time and multiply-and-accumulates(MACs) when compared with the binary segmentation models with similar performance in intersection-over-union(IoU).
{"title":"Facial Pore Segmentation Algorithm using Shallow CNN","authors":"Sunyong Seo, S. Yoo, Semin Kim, Daeun Yoon, Jonghan Lee","doi":"10.1109/CBMS55023.2022.00062","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00062","url":null,"abstract":"Poresare minute skin openings through which hair and sebum come out and appear as holes in the facial skin. Enlarged pore is one of the major concerns for people who care about their skin. Remedies include the use of cosmetics and pore-reduction medical procedures. Awareness of the condition of one's facial pores and appropriate management are required to prevent pore deterioration. Pore segmentation algorithms based on classical image processing are characterized by low accuracy and high computational costs. In addition, these algorithms require that input images be taken in light-controlled environments. These issues were resolved by using a light-specialized data augmentation method and a neural network with a narrow receptive field for identifying local features. We introduce Pore-Net, an algorithm that can be used on mobile devices to segment pores with a low computational cost, using selfie-camera images as an input. Pore-Net has the following algorithm flow. First, a confidence map-based segmentation without encoder-decoder form is applied to lower the computational costs on high-resolution input images. Second, pre- and post-processing for input based on region-of-interest(ROI) of facial landmarks are performed to work robustly in mobile devices. Pore-Net achieved the lowest computational cost in inference time and multiply-and-accumulates(MACs) when compared with the binary segmentation models with similar performance in intersection-over-union(IoU).","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"38 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120999447","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}
Pub Date : 2022-07-01DOI: 10.1109/CBMS55023.2022.00026
S. Hogue, Adrianna C. Shembel, X. Guo
Vocal strain can have a profound effect on a person's life and livelihood. However, methods to identify and quantify vocal strain presumed to originate in the laryngeal muscles severely lack. We aim to address this shortcoming. Using motion capture with consumer RGBD cameras, we track skin deformation of perilaryngeal anterior neck regions in participants with and without vocal strain. Neck movement variability differences between the two groups provides insight into extrinsic laryngeal vocal muscles that may underlie symptoms of vocal strain.
{"title":"Study of Vocal Muscle Strain with Skin Deformation Tracking System","authors":"S. Hogue, Adrianna C. Shembel, X. Guo","doi":"10.1109/CBMS55023.2022.00026","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00026","url":null,"abstract":"Vocal strain can have a profound effect on a person's life and livelihood. However, methods to identify and quantify vocal strain presumed to originate in the laryngeal muscles severely lack. We aim to address this shortcoming. Using motion capture with consumer RGBD cameras, we track skin deformation of perilaryngeal anterior neck regions in participants with and without vocal strain. Neck movement variability differences between the two groups provides insight into extrinsic laryngeal vocal muscles that may underlie symptoms of vocal strain.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131122264","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}
Pub Date : 2022-07-01DOI: 10.1109/CBMS55023.2022.00082
Daniel Gómez-Bravo, Aaron García, Guillermo Vigueras, Belén Ríos-Sánchez, B. Otero, R. López, M. Torrente, Ernestina Menasalvas Ruiz, M. Provencio, A. R. González
Lung cancer is the leading cause of cancer death. More than 236,740 new cases of lung cancer patients are expected in 2022, with an estimation of more than 130,180 deaths. Improving the survival rates or the patient's quality of life is partially covered by a common element: treatments. Cancer treatments are well known for the toxic outcomes and secondary effects on the patients. These toxicities cause different health problems that impact the patient's quality of life. Reducing toxicities without a decline on the positive survival effect is an important goal that aims to be pursued from the clinical perspective. On the other hand, clinical guidelines include general knowl-edge about cancer treatment recommendations to assist clinicians. Although they provide treatment recommendations based on cancer disease aspects and individual patient features, a statistical analysis taking into account treatment outcomes is not provided here. Therefore, the comparison between clinical guidelines with treatment patterns found in clinical data, would allow to validate the patterns found, as well as discovering alternative treatment patterns. In this work, we have analyzed a dataset containing lung cancer patients information including patients' data, prescribed treatments and outcomes obtained. Using a Subgroup Discovery method we identify patterns based on cancer stage while relying on treatment outcomes. Results are compared with clinical guide-lines and analyzed based on statistical and medical relevance using Subgroup Discovery metrics.
{"title":"Subgroup Discovery Analysis of Treatment Patterns in Lung Cancer Patients","authors":"Daniel Gómez-Bravo, Aaron García, Guillermo Vigueras, Belén Ríos-Sánchez, B. Otero, R. López, M. Torrente, Ernestina Menasalvas Ruiz, M. Provencio, A. R. González","doi":"10.1109/CBMS55023.2022.00082","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00082","url":null,"abstract":"Lung cancer is the leading cause of cancer death. More than 236,740 new cases of lung cancer patients are expected in 2022, with an estimation of more than 130,180 deaths. Improving the survival rates or the patient's quality of life is partially covered by a common element: treatments. Cancer treatments are well known for the toxic outcomes and secondary effects on the patients. These toxicities cause different health problems that impact the patient's quality of life. Reducing toxicities without a decline on the positive survival effect is an important goal that aims to be pursued from the clinical perspective. On the other hand, clinical guidelines include general knowl-edge about cancer treatment recommendations to assist clinicians. Although they provide treatment recommendations based on cancer disease aspects and individual patient features, a statistical analysis taking into account treatment outcomes is not provided here. Therefore, the comparison between clinical guidelines with treatment patterns found in clinical data, would allow to validate the patterns found, as well as discovering alternative treatment patterns. In this work, we have analyzed a dataset containing lung cancer patients information including patients' data, prescribed treatments and outcomes obtained. Using a Subgroup Discovery method we identify patterns based on cancer stage while relying on treatment outcomes. Results are compared with clinical guide-lines and analyzed based on statistical and medical relevance using Subgroup Discovery metrics.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114246057","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}
Pub Date : 2022-07-01DOI: 10.1109/CBMS55023.2022.00035
Yunlong Ye, Liang Xiao
The treatment of patients with multimorbidity has always been a matter of importance. Due to the complexity of patients' conditions, physicians need to consider not only the cumbersome consultation process and complex care plans., but also potential clinical decision conflicts between different diseases. Currently, most clinical guidelines focus on a single medical condition, and the emergent and random nature of illness in patients with multiple conditions makes it difficult to take good account of the potential conflicts between various clinical decisions. Current clinical decision models on the treatment of complications are limited to specific types of complications and usually detect conflicts in a declarative method, which is difficult to cover various types of clinical decision conflicts and is not scalable. We model the treatment process of patients with multimorbidity as a goal forest and propose a goal-driven clinical support model for group decision making. This model is applicable to distributed settings and can integrate multiple clinical guidelines to concurrently treat patients with multimorbidity. A clinical decision conflict ontology is constructed that defines various decision conflict types for clinical decision conflict detection, and providing solutions for conflict resolution.
{"title":"A goal-driven approach for clinical decision conflict detection and its application to the treatment of multimorbidity","authors":"Yunlong Ye, Liang Xiao","doi":"10.1109/CBMS55023.2022.00035","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00035","url":null,"abstract":"The treatment of patients with multimorbidity has always been a matter of importance. Due to the complexity of patients' conditions, physicians need to consider not only the cumbersome consultation process and complex care plans., but also potential clinical decision conflicts between different diseases. Currently, most clinical guidelines focus on a single medical condition, and the emergent and random nature of illness in patients with multiple conditions makes it difficult to take good account of the potential conflicts between various clinical decisions. Current clinical decision models on the treatment of complications are limited to specific types of complications and usually detect conflicts in a declarative method, which is difficult to cover various types of clinical decision conflicts and is not scalable. We model the treatment process of patients with multimorbidity as a goal forest and propose a goal-driven clinical support model for group decision making. This model is applicable to distributed settings and can integrate multiple clinical guidelines to concurrently treat patients with multimorbidity. A clinical decision conflict ontology is constructed that defines various decision conflict types for clinical decision conflict detection, and providing solutions for conflict resolution.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115140365","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}
Pub Date : 2022-07-01DOI: 10.1109/CBMS55023.2022.00047
Miro Schleicher, Sebastian Hamacher, Mats Naujoks, Kolja Günther, Timo Schmidt, R. Pryss, Johannes Schobel, W. Schlee, M. Spiliopoulou
Applications in mobile health (mHealth) empower self-monitoring of chronic conditions of the user and also offer insights to medical experts. The data generated by these apps constitute one time series per user. These time series vary substantially in length and contain ‘gaps’, as users pause or stop interacting with the app. In order to design measures that promote patient engagement with the app, it is necessary to predict and understand decline in engagement. We measured the performance of the algorithms on two real-world datasets from an mHealth app. We show that all approaches outperform the baseline and that shapelet, dictionary and matrix distance approach perform similarly for long-term prediction. This is particularly important because it allows early intervention towards increase of engagement. In this paper, we present an approach that uses the missingness information to process time series with large gaps.
{"title":"Prediction of declining engagement to self-monitoring apps on the example of tinnitus mHealth data","authors":"Miro Schleicher, Sebastian Hamacher, Mats Naujoks, Kolja Günther, Timo Schmidt, R. Pryss, Johannes Schobel, W. Schlee, M. Spiliopoulou","doi":"10.1109/CBMS55023.2022.00047","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00047","url":null,"abstract":"Applications in mobile health (mHealth) empower self-monitoring of chronic conditions of the user and also offer insights to medical experts. The data generated by these apps constitute one time series per user. These time series vary substantially in length and contain ‘gaps’, as users pause or stop interacting with the app. In order to design measures that promote patient engagement with the app, it is necessary to predict and understand decline in engagement. We measured the performance of the algorithms on two real-world datasets from an mHealth app. We show that all approaches outperform the baseline and that shapelet, dictionary and matrix distance approach perform similarly for long-term prediction. This is particularly important because it allows early intervention towards increase of engagement. In this paper, we present an approach that uses the missingness information to process time series with large gaps.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128308634","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}
Pub Date : 2022-07-01DOI: 10.1109/CBMS55023.2022.00069
Yi Li, Yuanyuan Zhao, Mingyu Wang, Fei Li, Jia Chen, Yanji Luo, S. Feng, Xiaoyi Lin, Bingsheng Huang
Diagnosis of tumors is an important direction of computer-aided diagnosis (CAD). The shape, size, and boundary of the tumor are widely-used diagnostic evidence, and the corresponding segmentation annotated by the radiologists is a vital expert knowledge, which can be used as supervision to guide feature extraction. Therefore, this study firstly introduces a multi-task learning (MTL) network integrating segmentation task for predicting grading of pancreatic neuroendocrine neoplasms (pNENs) and the microvascular invasion (MVI) of hepatocellular carcinoma (HCC). The proposed network combines a powerful split-attention-based encoder and a U-net decoder, and achieves the best performance in comparisons of other popular networks and previous studies. In addition, feature map visualization suggests that the reason for the improved classification performance may be that MTL makes the encoder pay more attention to lesions and extract more semantic information.
{"title":"Integrating with Segmentation by Using Multi-Task Learning Improves Classification Performance in Medical Image Analysis","authors":"Yi Li, Yuanyuan Zhao, Mingyu Wang, Fei Li, Jia Chen, Yanji Luo, S. Feng, Xiaoyi Lin, Bingsheng Huang","doi":"10.1109/CBMS55023.2022.00069","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00069","url":null,"abstract":"Diagnosis of tumors is an important direction of computer-aided diagnosis (CAD). The shape, size, and boundary of the tumor are widely-used diagnostic evidence, and the corresponding segmentation annotated by the radiologists is a vital expert knowledge, which can be used as supervision to guide feature extraction. Therefore, this study firstly introduces a multi-task learning (MTL) network integrating segmentation task for predicting grading of pancreatic neuroendocrine neoplasms (pNENs) and the microvascular invasion (MVI) of hepatocellular carcinoma (HCC). The proposed network combines a powerful split-attention-based encoder and a U-net decoder, and achieves the best performance in comparisons of other popular networks and previous studies. In addition, feature map visualization suggests that the reason for the improved classification performance may be that MTL makes the encoder pay more attention to lesions and extract more semantic information.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129393389","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}
Pub Date : 2022-07-01DOI: 10.1109/CBMS55023.2022.00023
J. Pereira, M. X. Ribeiro
Deep Learning has become increasingly frequent in the studies and analysis of medical images. Advances relevant to this area of research improve computer-aided diagnostic systems and help physicians' routine when providing a second opinion. Breast cancer is one of the types most common cancer among women worldwide. Early diagnosis of breast cancer can facilitate treatment and help saves lives. Mammography is the most widely used exam in the clinical routine to diagnose breast cancer. The analysis of the mammogram requires a specialist with experience in medical imaging. Deep Learning and Machine Learning techniques can collaborate computationally with this task. Adapting the hyperparameters provided to deep learning architectures helps improve the results in analyzing and classifying mammogram images. This paper presents a deep learning-based approach to classifying mammogram image regions of interest (ROIs). This approach includes transfer learning, hyperparameter and fine-tuning, and an ensemble with the models that showed the best results. The process demonstrated promising results, with the ensemble reaching 92% accuracy in the classification of mammogram ROIs of the test set and the area under the curve (AUC) value of 0.97 for the best model.
{"title":"Hyperparameter for Deep Learning Applied in Mammogram Image Classification","authors":"J. Pereira, M. X. Ribeiro","doi":"10.1109/CBMS55023.2022.00023","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00023","url":null,"abstract":"Deep Learning has become increasingly frequent in the studies and analysis of medical images. Advances relevant to this area of research improve computer-aided diagnostic systems and help physicians' routine when providing a second opinion. Breast cancer is one of the types most common cancer among women worldwide. Early diagnosis of breast cancer can facilitate treatment and help saves lives. Mammography is the most widely used exam in the clinical routine to diagnose breast cancer. The analysis of the mammogram requires a specialist with experience in medical imaging. Deep Learning and Machine Learning techniques can collaborate computationally with this task. Adapting the hyperparameters provided to deep learning architectures helps improve the results in analyzing and classifying mammogram images. This paper presents a deep learning-based approach to classifying mammogram image regions of interest (ROIs). This approach includes transfer learning, hyperparameter and fine-tuning, and an ensemble with the models that showed the best results. The process demonstrated promising results, with the ensemble reaching 92% accuracy in the classification of mammogram ROIs of the test set and the area under the curve (AUC) value of 0.97 for the best model.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122212502","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}
Pub Date : 2022-07-01DOI: 10.1109/CBMS55023.2022.00058
Meiyan Yue, Z. Dai, Jiahui He, Yaoqin Xie, N. Zaki, Wenjian Qin
In this paper, we propose a novel deep learning-based automatic delineation method of nasopharynx gross tumor volume (GTVnx) by combing computed tomography (CT) and magnetic resonance imaging (MRI) modalities. The purpose of this study is to explore whether MRI can provide additional information to improve the accuracy of delineation on CT. The proposed model can adaptively leverage the high contrast information of MRI into the automated delineation of GTVnx on CT in nasopharyngeal carcinoma (NPC) radiotherapy. In this study, the dataset collected from 192 patients with NPC was used to verify the performance of the proposed method. The average Dice Similarity Coefficient, 95% Hausdorff Distance and Average Symmetric Surface Distance of the segmentation results predicted by the proposed model are 0.7181, 9.6637mm, and 2.8014mm, respectively, which outperformed that of the single-modal and the concatenation-based multi-modal segmentation models.
{"title":"MRI-guided Automated Delineation of Gross Tumor Volume for Nasopharyngeal Carcinoma using Deep Learning","authors":"Meiyan Yue, Z. Dai, Jiahui He, Yaoqin Xie, N. Zaki, Wenjian Qin","doi":"10.1109/CBMS55023.2022.00058","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00058","url":null,"abstract":"In this paper, we propose a novel deep learning-based automatic delineation method of nasopharynx gross tumor volume (GTVnx) by combing computed tomography (CT) and magnetic resonance imaging (MRI) modalities. The purpose of this study is to explore whether MRI can provide additional information to improve the accuracy of delineation on CT. The proposed model can adaptively leverage the high contrast information of MRI into the automated delineation of GTVnx on CT in nasopharyngeal carcinoma (NPC) radiotherapy. In this study, the dataset collected from 192 patients with NPC was used to verify the performance of the proposed method. The average Dice Similarity Coefficient, 95% Hausdorff Distance and Average Symmetric Surface Distance of the segmentation results predicted by the proposed model are 0.7181, 9.6637mm, and 2.8014mm, respectively, which outperformed that of the single-modal and the concatenation-based multi-modal segmentation models.","PeriodicalId":218475,"journal":{"name":"2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125497193","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}
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