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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： S.S. Demir Joint Graduate Biomedical Engineering Program University of Memphis and University of Tennessee Memphis TN USA

Mentoring is significant personal and professional assistance given by a more experienced person to a less experienced person during a time of transition. Transitions from high school to university, from university to graduate school are difficult. Organizing and running mentoring programs in schools or in professional societies provide good recruitment and retention of female students in engineering. biomedical engineering (BME) is the engineering discipline that has the highest percentage of female degree recipients and tenure/tenure-track teaching faculty. Thus there is a great potential for female role models, mentors and mentees in BME. Recently, I have a developed a mentoring program for women at the joint graduate biomedical engineering program of University of Memphis (UM) and University of Tennessee (UT). Currently we are following the book "Giving Much/Gaining More: Mentoring for Success" by Dr. Wadsworth for our meetings and activities to provide a support and discussion group, and environment to women in their transition time of the BME graduate studies. Our future goal is to expand mentoring to female students in our engineering school.

关键词： Employee welfare biomedical engineering Educational institutions Books Agricultural engineering Education Chemical engineering Industrial engineering Recruitment Organizing

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Neural Networks and Explainable Artificial Intelligence for Breast Cancer Prediction and Classification 16

Neural Networks and Explainable Artificial Intelligence for ...

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16th International Conference on ENTERprise Information Systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information Systems and Technologies, HCist 2024

作者： Yahata, Erika Santos, Pablo Deoclecia Dos De Souza Pires, Maria Marlene Suyama, Ricardo Simoes, Priscyla Waleska Graduate Program in Information Engineering Federal University of ABC SP Santo André Brazil Graduate Program in Biomedical Engineering Federal University of ABC São Bernardo Do Campo Brazil Pediatrics Department Federal University of Santa Catarina Santa Catarina Brazil

Solutions based on Artificial Intelligence techniques have been proposed in the healthcare sector, however the lack of understanding of the results has been a cause for concern and one of the main barriers to their effective use. To fill the gap in understanding these models considered black boxes, the Explainable Artificial Intelligence was proposed to explain the relationship between input data and the prediction results made by the Artificial Intelligence models. In this study, we present the use of explainability techniques for a black box machine learning model in breast cancer classification. Breast cancer is the type of cancer that causes the most deaths among women in the world and early diagnosis is essential to increase the chances of survival. The study evaluated the explainability of breast tumor predictions based on the Multilayer Perceptron artificial neural networks algorithm, considering a sample that contained data from 164 female patients undergoing Core Biopsy in the southern region of Brazil. The SHAP, LIME, PDP and ICE methods were used for the global and local explainability of the predictions. The results showed that in the global assessment, the BI-RADS® 5 ultrasound and mammography attributes were considered the most important for predicting the malignant tumor. Nodule size greater than 2 cm, presence of a family history and palpable nodule were also considered important for the prediction. In the local evaluation, it was found that the model correctly classified the tumors considering different characteristics of the patients. Through the results presented, the methods of explicability show evidence that the predictive model for breast cancer can be interpreted and understood, overcoming the barrier of lack understanding of the results of a black box model. © 2025 Elsevier B.V.. All rights reserved.

关键词： Prediction models

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GeoRefineNet: A Multistage Framework for Enhanced Cephalometric Landmark Detection in CBCT Images Using 3D Geometric Information 17th

GeoRefineNet: A Multistage Framework for Enhanced Cephalome...

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17th Asian Conference on Computer Vision, ACCV 2024

作者： Viriyasaranon, Thanaporn Ma, Serie Choi, Jang-Hwan Department of Computational Medicine Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Division of Mechanical and Biomedical Engineering Graduate Program in System Health Science and Engineering Ewha Womans University Seoul Korea Republic of Department of Artificial Intelligence Ewha Womans University Seoul Korea Republic of

ISBN: (纸本)9789819609000

The precise detection of cephalometric landmarks on two-dimensional (2D) radiographs or three-dimensional (3D) computed tomography (CT) images is a fundamental step in various medical fields, especially in research on orthodontics and maxillofacial surgery. Deep learning-based detectors have demonstrated remarkable accuracy in 2D cephalometric analysis, whereas conventional single-view approaches are limited by their reliance on information from a single perspective. This study proposes GeoRefineNet, a novel multistage framework that leverages information from multiple CT scans acquired at various angles. By incorporating geometric knowledge through a 3D heatmap reconstruction process, GeoRefineNet improves robustness, accuracy, and adaptability to various cephalometric configurations. The proposed framework predicts 3D landmark positions on CT images, effectively addressing challenges associated with high-dimensional input data and limited training examples. GeoRefineNet surpasses the existing state-of-the-art models in the 2D and 3D domains, as demonstrated by its superior performance on numerical and clinical datasets. These findings indicate that GeoRefineNet offers a promising avenue for improving the accuracy and reliability of cephalometric landmark detection fostering further advances in clinical diagnosis and treatment planning. Our code is available at https://***/Thanaporn09/***. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computerized tomography

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Brain age estimation:premise,promise,and problems

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Neural Regeneration Research 2025年第8期20卷 2313-2314页

作者： Jarrad Perron Ji Hyun Ko Graduate Program in Biomedical Engineering Price Faculty of EngineeringUniversity of ManitobaWinnipegMBCanada PrairieNeuro Research Centre Kleysen Institute for Advanced MedicineHealth Sciences CentreWinnipegMBCanada Department of Human Anatomy and Cell Science Rady Faculty of Health SciencesUniversity of ManitobaWinnipegMBCanada

Premise:The com bined effects of modern healthcare practices which prolong lifespan and declining birthrates have created unprecedented changes in age demographics worldwide that are especially pronounced in Japan,South Korea,Europe,and North *** old age is the most significant predictor of dementia,global healthcare systems must rise to the challenge of providing care for those with neurodegenerative disorders.

关键词： estimation providing birth

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3D-Printable Photothermal and Temperature-Controlled Polycaprolactone Scaffolds Incorporating Gold Plasmonic Blackbodies for Bone Tissue engineering

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ACS Applied Materials and Interfaces 2025年第20期17卷 29455-29468页

作者： Chen, Chieh-Ying Hope Gadia Moreno, Ruaina Lily Wang, Po-Yao Nguyen, Thanh Sang Wu, Jia-Lin Chen, Kuan-Hao Chen, Chih-Hwa Lin, Chia-Ying Wong, Pei-Chun Graduate Institute of Biomedical Optomechatronics College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan School of Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Graduate Institute of Biomedical Materials and Tissue Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Orthopedics Taipei Medical University Hospital Taipei11031 Taiwan International Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Department of Trauma Hue Central Hospital Hue530000 Viet Nam Department of Orthopedics School of Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Orthopedics Research Center Taipei Medical University Hospital Taipei11031 Taiwan Centers for Regional Anesthesia and Pain Medicine Wan Fang Hospital Taipei Medical University Taipei11096 Taiwan Department of Orthopedics Shuang Ho Hospital Taipei Medical University New Taipei23561 Taiwan Convergent Bioscience and Technology Institute Department of Biomedical Engineering and Informatics Indiana University IndianapolisIN46202 United States

Three-dimensional (3D) printing technology has revolutionized the design and fabrication of bone scaffolds, offering precise and customizable solutions for bone tissue engineering. In this study, we developed polycaprolactone (PCL) scaffolds that incorporated gold plasmonic blackbodies (AuPBs) to harness photothermal properties for temperature-controlled bone regeneration. The AuPB-PCL scaffolds demonstrated enhanced mechanical strength, a tunable thermal response under near-infrared (NIR) laser irradiation, and improved osteogenic potential. Photothermal stimulation effectively modulated cellular responses, promoting osteoblast proliferation, alkaline phosphatase (ALP) activity, and mineralization. Notably, mild hyperthermia (39-41 °C) induced by laser irradiation optimized osteogenesis, while excessive temperatures (≥42.5 °C) impaired cellular function due to mitochondrial stress and oxidative damage. These findings highlight the potential of AuPB-PCL scaffolds for controlled photothermal bone regeneration, offering a promising strategy for precise, completely noninvasive stimulation of bone repair. © 2025 The Authors. Published by American Chemical Society.

关键词： Fiber bonding

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View-Invariant Object Representation in Anterior and Posterior Inferotemporal Cortex: A Machine Learning Approach

SSRN

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SSRN 2025年

作者： Okamura, Jun-Ya Fukano, Daisuke Wang, Gang Information Science and Biomedical Engineering Program Graduate School of Science and Engineering Kagoshima University Kagoshima890-0065 Japan

Inferotemporal (IT) cortex is the final visual area in the ventral stream where object information is processed. Previous electrophysiological studies showed viewing angle tolerance of 30-60° of single IT cells to the objects experienced in discrimination at each of several viewing angles, and to the objects experienced in learning association of different views. IT is divided into anterior (cytoarchitectonic area TE) and posterior (TEO) parts. It was reported that single cells in area TE showed the viewing angle tolerance while those in area TEO did not. In the present study population activities were compared between cell populations in area TE and those in area TEO using machine learning algorithm. An object set consisted of four similar objects created by deforming a prototype object, and four views each separated by 30°. A population vector was created by aligning responses of the cells to each object image. A classifier was trained by support vector machine (SVM) to create a hyperplane that separated one object from the other three objects at the same viewing angles, and then tested by response vectors to the object images at different viewing angles. In area TE, dynamics of the performance evaluated by d’ showed viewing angle tolerance of 30-60° to the objects with prior experience in learning association of different views. In area TEO, populations of the cells did not show the viewing angle tolerance. Significant increase of the d’ values in area TE in the late time period for the objects with prior experience in learning association of different views may suggest view-invariance is more represented in late time period than early time period. These results suggest that viewpoint invariance is expressed more strongly in the TE region, providing new insights into the neural basis of object recognition © 2025, The Authors. All rights reserved.

关键词： Support vector machines

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Generalize or Specialize? The Effect of Coil Combining in Deep Learning Based MR Image Reconstruction

Generalize or Specialize? The Effect of Coil Combining in De...

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IEEE International Symposium on biomedical Imaging

作者： Natalia Dubljevic Richard Frayne Roberto Souza Biomedical Engineering Graduate Program Hotchkiss Brain Institute University of Calgary Calgary AB Canada Seaman Family MR Research Centre Foothills Medical Centre Calgary AB Canada Biomedical Engineering Graduate Program Radiology Clinical Neurosciences Hotchkiss Brain Institute University of Calgary Calgary AB Canada Biomedical Engineering Graduate Program Electrical and Software Engineering Hotchkiss Brain Institute University of Calgary Calgary AB Canada

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

With the advent of deep-learning based MR image reconstruction, new questions are raised regarding best design choices. In particular, when using recent reconstruction models that have been developed for processing multi-channel MR data. A fundamental question is whether to first combine channels to improve model generalizability (i.e., a “coil-combined” approach) or to keep channel processing separate to fully utilize multi-channel information (i.e., an “all-coil” approach). In this work, we compare three popular architectures using coil-combined and all-coil styles on brain MR images. All-coil styles improved in-distribution performance, such as when reconstructing only presumed healthy individu-als. Coil-combined designs better generalized to unseen data from patients with pathology.

关键词： Deep learning Coils Pathology Codes Magnetic resonance Brain modeling Data models Image reconstruction biomedical imaging

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Nanoplasmonic Biosensors: A Comprehensive Overview and Future Prospects

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International Journal of Nanomedicine 2025年 20卷 5817-5836页

作者： Mutalik, Chinmaya Sharma, Shashwat Yougbaré, Sibidou Chen, Chih-Yu Kuo, Tsung-Rong Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei 11031 Taiwan Center for Airborne Infection & Transmission Science Tulane University School of Medicine New Orleans 70112 LA United States International Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei 11031 Taiwan Institut de Recherche en Sciences de La Santé/Direction Régionale du Centre Ouest (IRSS/DRCO) BP 21811 Nanoro Burkina Faso Department of Orthopedics Shuang Ho Hospital Taipei Medical University New Taipei City 23561 Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei 11031 Taiwan School of Biomedical Engineering Taipei Medical University Taipei 11031 Taiwan

Recent nanotechnological advancements have resulted in a paradigm shift in biosensing applications through the advent of nanoplasmonic biosensors. These devices integrate nanomaterials with phenomena like surface plasmon resonance (SPR) and localized SPR (LSPR) to address the critical diagnostic and analytical needs across medicine, food safety, and drug discovery. Leveraging metals like gold and silver, these sensors exhibit enhanced optical and electronic properties, enabling the detection of biomolecules at ultralow concentrations. However, despite their transformative potential, challenges concerning stability, reproducibility, cost-efficiency, and scalability impede widespread implementation. This review offers a rigorous analysis of nanoplasmonic biosensors, emphasizing their underlying operational mechanisms and diverse applications. It also delves into design paradigms, fabrication protocols, and optimization strategies while concurrently examining prevailing challenges and prospective advancements. Furthermore, it highlights emerging trends, such as hybrid plasmonic nanostructures, conferring advantages in miniaturization, automation, and high-throughput analysis, thereby establishing a robust foundation for future innovation in the field. © 2025 Mutalik et al.

关键词： device nanomaterial nanoplasmonic optical biosensor SPR

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Spatio-Temporal Deep Learning for Final Infarct Prediction Using Acute Stroke CT Perfusion Data 4th

Spatio-Temporal Deep Learning for Final Infarct Prediction U...

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4th International Workshop on Imaging and Treatment Challenges, SWITCH 2024, and 6th International Challenge on Ischemic Stroke Lesion Segmentation Challenge, ISLES 2024, Held in Conjunction with Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Amador, Kimberly Winder, Anthony J. Forkert, Nils D. Biomedical Engineering Graduate Program University of Calgary Calgary Canada Department of Radiology University of Calgary Calgary Canada Hotchkiss Brain Institute University of Calgary Calgary Canada Alberta Children’s Hospital Research Institute University of Calgary Calgary Canada

ISBN: (纸本)9783031811005

Accurate prediction of the tissue outcome is crucial for guiding treatment decisions in acute ischemic stroke (AIS). Spatio-temporal (4D) Computed Tomography Perfusion (CTP) provides detailed insights into cerebral blood flow dynamics, which are essential for predicting final infarct regions. However, its high-dimensional and noisy nature presents challenges for direct prediction. In this study, we evaluate a deep learning model that fully leverages 4D CTP data for predicting tissue outcomes. The model integrates a shared-weight convolutional neural network (CNN) encoder, a Transformer encoder, and a CNN decoder to capture both spatial and temporal dependencies within the data. We evaluated this approach on a multicenter dataset of 143 patients from the ISLES 2024 challenge. The results reveal a Dice score of 0.20, an absolute volume difference of 17 ml, a mean lesion count difference of 19, and a lesion-wise F1-Score of 0.02, underscoring both the potential and challenges of directly utilizing 4D CTP data for final infarct prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Neural Networks and Explainable Artificial Intelligence for Breast Cancer Prediction and Classification

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Procedia Computer Science 2025年 256卷 1159-1166页

作者： Erika Yahata Pablo Deoclecia dos Santos Maria Marlene de Souza Pires Ricardo Suyama Priscyla Waleska Simoes Graduate Program in Information Engineering Federal University of ABC Santo André SP Brazil Graduate Program in Biomedical Engineering Federal University of ABC São Bernardo do Campo SP Brazil Pediatrics Department Federal University of Santa Catarina Florianópolis Santa Catarina Brazil

关键词： Explainable Artificial Intelligence Predictive Model Explainability Machine Learning Breast Cancer

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