Proceedings. IEEE International Symposium on Computer-Based Medical Systems最新文献

Automated Deep Learning Approach for Post-Operative Neonatal Pain Detection and Prediction through Physiological Signals. 基于生理信号的新生儿术后疼痛检测与预测的自动深度学习方法。

Proceedings. IEEE International Symposium on Computer-Based Medical Systems Pub Date : 2025-06-01 Epub Date: 2025-07-04 DOI: 10.1109/cbms65348.2025.00164

Jacqueline Hausmann, Jiayi Wang, Marcia Kneusel, Stephanie Prescott, Peter R Mouton, Yu Sun, Dmitry Goldgof

{"title":"Automated Deep Learning Approach for Post-Operative Neonatal Pain Detection and Prediction through Physiological Signals.","authors":"Jacqueline Hausmann, Jiayi Wang, Marcia Kneusel, Stephanie Prescott, Peter R Mouton, Yu Sun, Dmitry Goldgof","doi":"10.1109/cbms65348.2025.00164","DOIUrl":"10.1109/cbms65348.2025.00164","url":null,"abstract":"It is well-known that severe pain and powerful pain medications cause short- and long-term damage to the developing nervous system of newborns. Caregivers routinely use physiological vital signs [Heart Rate (HR), Respiration Rate (RR), Oxygen Saturation (SR)] to monitor post-surgical pain in the Neonatal Intensive Care Unit (NICU). Here we present a novel approach that combines continuous, non-invasive monitoring of these vital signs and Computer Vision/Deep Learning to make automatic neonate pain detection with an accuracy of 74% AUC, 67.59% mAP. Further, we report for the first time our Early Pain Detection (EPD) approach that explores prediction of the time to onset of post-surgical pain in neonates. Our EPD can alert NICU workers to postoperative neonatal pain about 5 to 10 minutes prior to pain onset. In addition to alleviating the need for intermittent pain assessments by busy NICU nurses via long-term observation, our EPD approach creates a time window prior to pain onset for the use of less harmful pain mitigation strategies. Through effective pain mitigation prior to spinal sensitization, EPD could minimize or eliminate severe post-surgical pain and the consequential need for powerful analgesics in post-surgical neonates.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"2025 ","pages":"801-806"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12444759/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Few-Shot Prompting with Vision Language Model for Pain Classification in Infant Cry Sounds. 基于视觉语言模型的短时提示婴儿哭声疼痛分类。

Proceedings. IEEE International Symposium on Computer-Based Medical Systems Pub Date : 2025-06-01 Epub Date: 2025-07-04 DOI: 10.1109/cbms65348.2025.00174

Anthony McCofie, Abhiram Kandiyana, Peter R Mouton, Yu Sun, Dmitry Goldgof

引用次数: 0

Artificial Intelligence Assurance in Head and Neck Surgery: Now and Next. 头颈外科的人工智能保障：现在和未来。

Proceedings. IEEE International Symposium on Computer-Based Medical Systems Pub Date : 2025-06-01 Epub Date: 2025-07-04 DOI: 10.1109/cbms65348.2025.00195

Yuansan Liu, Sudanthi Wijewickrema, Bridget Copson, Jean-Marc Gerard, Sameer Antani

{"title":"Artificial Intelligence Assurance in Head and Neck Surgery: Now and Next.","authors":"Yuansan Liu, Sudanthi Wijewickrema, Bridget Copson, Jean-Marc Gerard, Sameer Antani","doi":"10.1109/cbms65348.2025.00195","DOIUrl":"https://doi.org/10.1109/cbms65348.2025.00195","url":null,"abstract":"Artificial intelligence (AI) is making significant advances toward becoming a well-established and promise-bearing technology in various medical domains such as screening, diagnostics, and biopharma research. However, its state remains relatively nascent in surgery and surgical therapeutics. This presents an opportunity for leveraging ongoing rapid advances in AI technology and the increasing availability of large, diverse datasets to pave the way for their use in these domains. Expanding the use of AI to include various processes in surgery-related workflows could provide several benefits, such as greater assurance for reduced errors, better assistance to surgeons, and overall improved patient outcomes. To encourage further research in surgical AI, this article summarizes the state-of-the-art in AI assurance in various aspects of a patient's timeline when undergoing head and neck surgeries, including diagnostics, preoperative considerations, intraoperative guidance, and postoperative and outcome predictions. The work aims to highlight gaps in the state-of-the-art and identify opportunities for the computer-based medical systems community to encourage future research and development on the subject.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"2025 ","pages":"977-982"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12369650/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Hidden Threat of Hallucinations in Binary Chest X-ray Pneumonia Classification. 肺炎胸片二元分型中幻觉的潜在威胁。

Proceedings. IEEE International Symposium on Computer-Based Medical Systems Pub Date : 2025-06-01 Epub Date: 2025-07-04 DOI: 10.1109/cbms65348.2025.00138

Sivaramakrishnan Rajaraman, Zhaohui Liang, Niccolo Marini, Zhiyun Xue, Sameer Antani

{"title":"The Hidden Threat of Hallucinations in Binary Chest X-ray Pneumonia Classification.","authors":"Sivaramakrishnan Rajaraman, Zhaohui Liang, Niccolo Marini, Zhiyun Xue, Sameer Antani","doi":"10.1109/cbms65348.2025.00138","DOIUrl":"https://doi.org/10.1109/cbms65348.2025.00138","url":null,"abstract":"Hallucination in deep learning (DL) classification, where DL models yield confidently erroneous predictions remains a pressing concern. This study investigates whether binary classifiers are truly learning disease-specific features when distinguishing overlapping radiological presentations among pneumonia subtypes on chest X-ray (CXR) images. Specifically, we evaluate if uncertainty measure is a valuable tool in classifying signs of different pathogen-specific subtypes of pneumonia. We evaluated two binary classifiers to classify bacterial pneumonia and viral pneumonia, respectively, from normal CXRs. A third classifier explored the ability to distinguish bacterial from viral pneumonia presentation to highlight our concern regarding the observed hallucinations in the former cases. Our comprehensive analysis computes the Matthews Correlation Coefficient and prediction entropy metrics on a pediatric CXR dataset and reveals that the normal/bacterial and normal/viral classifiers consistently and confidently misclassify the unseen pneumonia subtype to their respective disease class. These findings expose a critical limitation concerning the tendency of binary classifiers to hallucinate by relying on general pneumonia indicators rather than pathogen-specific patterns, thereby challenging their utility in clinical workflows.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"2025 ","pages":"668-673"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12369649/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144981943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Concept-Based Explanation with Vision-Language Models. 用视觉语言模型增强基于概念的解释。

Proceedings. IEEE International Symposium on Computer-Based Medical Systems Pub Date : 2024-06-01 Epub Date: 2024-07-25 DOI: 10.1109/CBMS61543.2024.00044

Imran Hossain, Ghada Zamzmi, Peter Mouton, Yu Sun, Dmitry Goldgof

{"title":"Enhancing Concept-Based Explanation with Vision-Language Models.","authors":"Imran Hossain, Ghada Zamzmi, Peter Mouton, Yu Sun, Dmitry Goldgof","doi":"10.1109/CBMS61543.2024.00044","DOIUrl":"10.1109/CBMS61543.2024.00044","url":null,"abstract":"Although concept-based approaches are widely used to explain a model's behavior and assess the contributions of different concepts in decision-making, identifying relevant concepts can be challenging for non-experts. This paper introduces a novel method that simplifies concept selection by leveraging the capabilities of a state-of-the-art large Vision-Language Model (VLM). Our method employs a VLM to select textual concepts that describe the classes in the target dataset. We then transform these influential textual concepts into human-readable image concepts using a text-to-image model. This process allows us to explain the targeted network in a post-hoc manner. Further, we use directional derivatives and concept activation vectors to quantify the importance of the generated concepts. We evaluate our method on a neonatal pain classification task, analyzing the sensitivity of the model's output for the generated concepts. The results demonstrate that the VLM not only generates coherent and meaningful concepts that are easily understandable by non-experts but also achieves performance comparable to that of natural image concepts without the need for additional annotation costs.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"2024 ","pages":"219-224"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12458896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145152251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of MRI-Induced Power Absorption in Patients with DBS Leads. 脑起搏器导联患者mri诱导的能量吸收预测。

Proceedings. IEEE International Symposium on Computer-Based Medical Systems Pub Date : 2024-06-01 Epub Date: 2024-07-05 DOI: 10.1109/cbms61543.2024.00087

Yalcin Tur, Jasmine Vu, Selam Waktola, Alpay Medetalibeyoglu, Laleh Golestanirad, Ulas Bagci

{"title":"Prediction of MRI-Induced Power Absorption in Patients with DBS Leads.","authors":"Yalcin Tur, Jasmine Vu, Selam Waktola, Alpay Medetalibeyoglu, Laleh Golestanirad, Ulas Bagci","doi":"10.1109/cbms61543.2024.00087","DOIUrl":"10.1109/cbms61543.2024.00087","url":null,"abstract":"The interaction between deep brain stimulation (DBS) systems and magnetic resonance imaging (MRI) can induce tissue heating in patients. While electromagnetic (EM) simulations can be used to estimate the specific absorption rate (SAR) values in the presence of an implanted DBS system, they are computationally expensive. To address this drawback, we predict local SAR values in the tips of DBS leads with machine learning based efficient algorithms, specifically XgBoost and deep learning. We significantly outperformed the previous state of the art, and adapted new machine learning models based on Residual Networks family as well as XgBoost models. We observed that already extracted limited features are better suited for ensemble learning via XgBoost than deep networks due the small-data regime. Although we conclude that boosting gradient algorithm is more suitable for this non-linear regression problem due to structured nature of the data and small data regime, we found that width plays a more critical role than depth in network design and it has a strong potential for future research. Our experimental results, using a dataset of 260 instances that are patient-derived and artificial, reached an outstanding RMSE of 17.8 W/kg with XgBoost, 78 W/kg with deep networks, given that the previous study on this problem reached a state-of-the-art root mean square error value (RMSE) of 168 W/kg.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"2024 ","pages":"490-495"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12477686/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145202461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated Design of Task-Dedicated Illumination with Particle Swarm Optimization 基于粒子群优化的任务专用照明自动设计

Proceedings. IEEE International Symposium on Computer-Based Medical Systems Pub Date : 2023-01-01 DOI: 10.1109/CBMS58004.2023.00254

Austin Ryan English

引用次数: 0

Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network. 基于焦调制引导卷积神经网络的视频胶囊内窥镜分类。

Proceedings. IEEE International Symposium on Computer-Based Medical Systems Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00064

Abhishek Srivastava, Nikhil Kumar Tomar, Ulas Bagci, Debesh Jha

{"title":"Video Capsule Endoscopy Classification using Focal Modulation Guided Convolutional Neural Network.","authors":"Abhishek Srivastava, Nikhil Kumar Tomar, Ulas Bagci, Debesh Jha","doi":"10.1109/CBMS55023.2022.00064","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00064","url":null,"abstract":"Video capsule endoscopy is a hot topic in computer vision and medicine. Deep learning can have a positive impact on the future of video capsule endoscopy technology. It can improve the anomaly detection rate, reduce physicians' time for screening, and aid in real-world clinical analysis. Computer-Aided diagnosis (CADx) classification system for video capsule endoscopy has shown a great promise for further improvement. For example, detection of cancerous polyp and bleeding can lead to swift medical response and improve the survival rate of the patients. To this end, an automated CADx system must have high throughput and decent accuracy. In this study, we propose FocalConvNet, a focal modulation network integrated with lightweight convolutional layers for the classification of small bowel anatomical landmarks and luminal findings. FocalConvNet leverages focal modulation to attain global context and allows global-local spatial interactions throughout the forward pass. Moreover, the convolutional block with its intrinsic inductive/learning bias and capacity to extract hierarchical features allows our FocalConvNet to achieve favourable results with high throughput. We compare our FocalConvNet with other state-of-the-art (SOTA) on Kvasir-Capsule, a large-scale VCE dataset with 44,228 frames with 13 classes of different anomalies. We achieved the weighted F1-score, recall and Matthews correlation coefficient (MCC) of 0.6734, 0.6373 and 0.2974, respectively, outperforming SOTA methodologies. Further, we obtained the highest throughput of 148.02 images/second rate to establish the potential of FocalConvNet in a real-time clinical environment. The code of the proposed FocalConvNet is available at https://github.com/NoviceMAn-prog/FocalConvNet.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"2022 ","pages":"323-328"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9914988/pdf/nihms-1871537.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10708367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network. 基于多核扩展卷积网络的息肉自动分割。

Proceedings. IEEE International Symposium on Computer-Based Medical Systems Pub Date : 2022-07-01 DOI: 10.1109/CBMS55023.2022.00063

Nikhil Kumar Tomar, Abhishek Srivastava, Ulas Bagci, Debesh Jha

{"title":"Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network.","authors":"Nikhil Kumar Tomar, Abhishek Srivastava, Ulas Bagci, Debesh Jha","doi":"10.1109/CBMS55023.2022.00063","DOIUrl":"https://doi.org/10.1109/CBMS55023.2022.00063","url":null,"abstract":"The detection and removal of precancerous polyps through colonoscopy is the primary technique for the prevention of colorectal cancer worldwide. However, the miss rate of colorectal polyp varies significantly among the endoscopists. It is well known that a computer-aided diagnosis (CAD) system can assist endoscopists in detecting colon polyps and minimize the variation among endoscopists. In this study, we introduce a novel deep learning architecture, named MKDCNet, for automatic polyp segmentation robust to significant changes in polyp data distribution. MKDCNet is simply an encoder-decoder neural network that uses the pre-trained ResNet50 as the encoder and novel multiple kernel dilated convolution (MKDC) block that expands the field of view to learn more robust and heterogeneous representation. Extensive experiments on four publicly available polyp datasets and cell nuclei dataset show that the proposed MKDCNet outperforms the state-of-the-art methods when trained and tested on the same dataset as well when tested on unseen polyp datasets from different distributions. With rich results, we demonstrated the robustness of the proposed architecture. From an efficiency perspective, our algorithm can process at (≈ 45) frames per second on RTX 3090 GPU. MKDCNet can be a strong benchmark for building real-time systems for clinical colonoscopies. The code of the proposed MKDCNet is available at https://github.com/nikhilroxtomar/MKDCNet.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"2022 ","pages":"317-322"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9921313/pdf/nihms-1871530.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10708366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Mental Health Ubiquitous Monitoring: Detecting Context-Enriched Sociability Patterns Through Complex Event Processing 心理健康无所不在监测:通过复杂事件处理检测情境丰富的社交模式

Proceedings. IEEE International Symposium on Computer-Based Medical Systems Pub Date : 2020-07-01 DOI: 10.1109/CBMS49503.2020.00052

I. Moura, Francisco Silva, L. Coutinho, A. Teles

{"title":"Mental Health Ubiquitous Monitoring: Detecting Context-Enriched Sociability Patterns Through Complex Event Processing","authors":"I. Moura, Francisco Silva, L. Coutinho, A. Teles","doi":"10.1109/CBMS49503.2020.00052","DOIUrl":"https://doi.org/10.1109/CBMS49503.2020.00052","url":null,"abstract":"Traditionally, the process of monitoring and evaluating social behavior related to mental health has based on self-reported information, which is limited by the subjective character of responses and by various cognitive biases. Today, however, computational methods can use ubiquitous devices to monitor social behaviors related to mental health rather than relying on self-reports. Therefore, these technologies can be used to identify the routine of social activities, which enables the recognition of abnormal behaviors that may be indicative of mental disorders. In this paper, we present a solution for detecting context-enriched sociability patterns. Specifically, we introduced an algorithm capable of recognizing the social routine of monitored people. To implement the proposed algorithm, it was used a set of Complex Event Processing (CEP) rules, which allow the continuous processing of the social data stream derived from ubiquitous devices. The experiments performed indicated that the proposed solution is capable of detecting sociability patterns similar to a batch algorithm and demonstrated that context-based recognition provides a better understanding of social routine.","PeriodicalId":74567,"journal":{"name":"Proceedings. IEEE International Symposium on Computer-Based Medical Systems","volume":"19 1","pages":"239-244"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74119568","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