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Explainable Feature Embeddings from Histopathology Foundation Models: A Case Study for End Stage Kidney Disease Risk Analysis in Diabetic Nephropathy Patients

Explainable Feature Embeddings from Histopathology Foundatio...

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Medical Imaging 2025: Digital and Computational Pathology

作者： Kasireddy, Harishwar Reddy Lucarelli, Nicholas Yun, Donghwan Moon, Kyung Chul La Rosa, Patricio S. Tomaszewski, John E. Han, Seung Seok Shickel, Benjamin Naglah, Ahmed Sarder, Pinaki Dept. of Electrical and Computer Engineering Univ. of Florida GainesvilleFL United States Univ. of Florida Intelligent Critical Care Center GainesvilleFL United States Dept. of Biomedical Engineering Univ. of Florida GainesvilleFL United States Dept. of Internal Medicine Seoul National Univ. Seoul Korea Republic of Dept. of Pathology Seoul National Univ. Seoul Korea Republic of Seed Production Innovation Crop Science Division Bayer Company St. LouisMO United States Dept. of Pathology and Anatomical Sciences Univ. at Buffalo BuffaloNY United States Division of Nephrology Hypertension and Renal Transplantation Quantitative Health Section Dept. of Medicine Univ. of Florida GainesvilleFL United States

ISBN: (纸本)9781510686045

Foundational models (FMs) based on advanced neural network architectures have demonstrated improved performance in pathology image analysis across various organs due to their increased generalizability. However, their clinical adoption requires explainability, as their black box nature limits transparency. Understanding the specific features these models learn for a given downstream task is crucial for explainability and integrating FMs into clinical workflows more effectively. We propose a computational pipeline that enhances explainability by correlating domain-specific handcrafted features (HFs), with hidden features i.e., feature embeddings (FEs) from FMs. We correlate and combine HFs from Detectron 2 DeepLabv3+ segmentation with FEs from Prov-Gigapath (PG) and UNI FMs for improved explainability and performance. In this work, HFs are extracted from segmented functional tissue units, including arteries, tubules, globally sclerotic glomeruli, and non-globally sclerotic glomeruli. FEs are extracted at the tile and slide levels for PG and at the tile level for UNI. We use the Pearson correlation coefficient to identify significant correspondences between these feature sets. To evaluate our proposed methodology, we use 56 diabetic nephropathy kidney biopsy whole slide images (WSIs) from Seoul National University Hospital. The task is to predict end-stage kidney disease (ESKD) two years post-biopsy using leave-one-out cross-validation on 56 WSIs, with 16 from ESKD patients and 40 from non-ESKD patients. We combine top correlated features from FEs of FMs with HFs and train logistic regression (LR) and k nearest neighbor (kNN) classifiers. LR model trained on combined feature set improved accuracy, balanced accuracy, Matthew's correlation coefficient, F1-score, precision, and recall to 0.8393, 0.7938, 0.5993, 0.8377, 0.8367, 0.8393 respectively, when compared to LR and kNN models trained on individual feature sets. PG excelled in specificity (1.000) and AUROC (0.8281),

关键词： Biopsy

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Adaptive Aggregation Weights for Federated Segmentation of Pancreas MRI

Adaptive Aggregation Weights for Federated Segmentation of P...

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IEEE International Symposium on biomedical Imaging

作者： Hongyi Pan Gorkem Durak Zheyuan Zhang Yavuz Taktak Elif Keles Halil Ertugrul Aktas Alpay Medetalibeyoglu Yury Velichko Concetto Spampinato Ivo Schoots Marco J. Bruno Rajesh N. Keswani Pallavi Tiwari Candice Bolan Tamas Gonda Michael G. GORRins Michael B. Wallace Ziyue Xu UIas Bagci Department of Radiology Northwestern University Chicago IL USA Department of Radiology Istanbul Faculty of Medicine Istanbul University Istanbul Turkey Department of Internal Medicine Istanbul Faculty of Medicine Istanbul University Istanbul Turkey Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy Department of Radiology and Nuclear Medicine Erasmus Medical Center Rotterdam Netherlands Department of Gastroenterology and Hepatology Erasmus Medical Center Rotterdam Netherlands Department of Gastroenterology and Henatology Northwestern University Chicago IL USA Department of Biomedical Engineering and-Radiology University of Wisconsin Madison WI USA Division of Gastroenterology and Hepatology Mayo Clinic Jacksonville FL USA Division of Gastroenterology and Hepatology New York University New York NY USA Division of Gastroenterology and Henatology Johns Hopkins University Baltimore MD USA NVIDIA Bethesda MD USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face difficulties in general-izing across domains due to variations in imaging protocols and pa-tient demographics across institutions. This challenge is particularly evident in pancreas MRI segmentation, where anatomical variability and imaging artifacts significantly impact performance. In this paper, we conduct a comprehensive evaluation of FL algorithms for pancreas MRI segmentation and introduce a novel approach that in-corporates adaptive aggregation weights. By dynamically adjusting the contribution of each client during model aggregation, our method accounts for domain-specific differences and improves generalization across heterogeneous datasets. Experimental results demonstrate that our approach enhances segmentation accuracy and reduces the impact of domain shift compared to conventional FL methods while maintaining privacy-preserving capabilities. Significant performance improvements are observed across multiple hospitals (centers).

关键词： Training Image segmentation Adaptation models Protocols Accuracy Federated learning Magnetic resonance imaging Data models Pancreas biomedical imaging

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Consensus Statement on Brillouin Light Scattering Microscopy of Biological Materials

arXiv

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arXiv 2024年

作者： Bouvet, Pierre Bevilacqua, Carlo Ambekar, Yogeshwari Antonacci, Giuseppe Au, Joshua Caponi, Silvia Chagnon-Lessard, Sophie Czarske, Juergen Dehoux, Thomas Fioretto, Daniele Fu, Yujian Guck, Jochen Hamann, Thorsten Heinemann, Dag Jähnke, Torsten Jean-Ruel, Hubert Kabakova, Irina Koski, Kristie Koukourakis, Nektarios Krause, David La Cavera, Salvatore Landes, Timm Li, Jinhao Margueritat, Jeremie Mattarelli, Maurizio Monaghan, Michael Overby, Darryl R. Perez-Cota, Fernando Pontecorvo, Emanuele Prevedel, Robert Ruocco, Giancarlo Sandercock, John Scarcelli, Giuliano Scarponi, Filippo Testi, Claudia Török, Peter Vovard, Lucie Weninger, Wolfgang Yakovlev, Vladislav Yun, Seok-Hyun Zhang, Jitao Palombo, Francesca Bilenca, Alberto Elsayad, Kareem Center for Anatomy and Cell Biology Medical University of Vienna Austria Cell Biology and Biophysics Unit European Molecular Biology Laboratory Germany Fischell Department of Bioengineering University of Maryland United States Specto Srl. Milan Italy Unità di Perugia Italy LightMachinery Inc. Canada Departement of Electronics Carleton University Canada TU Dresden Germany Cluster of Excellence Physics of Life TU Dresden Germany Competence Center for Biomedical Computational Laser Systems TU Dresden Germany Institut Lumière Matière UMR5306 Université Lyon 1 CNRS Université de Lyon France Dipartimento di Fisica e Geologia Università di Perugia Italy Max Planck Institute for the Science of Light Erlangen Germany Department of Biology Norwegian University of Science and Technology Trondheim Norway Hannover Centre for Optical Technologies Leibniz University Hannover Germany CellSense Technologies GmbH Berlin Germany School of Mathematical and Physical Sciences University of Technology Sydney Australia Department of Chemistry University of California Davis United States Optics & Photonics Group Faculty of Engineering University of Nottingham United Kingdom Discipline of Mechanical Manufacturing & Biomedical Engineering Trinity College Dublin Ireland Department of Bioengineering Imperial College London United Kingdom CREST Optics S.p.A. Rome Italy Heidelberg Germany Center for Life Nano- & Neuro-Science Istituto Italiano di Tecnologia Rome Italy The Table Stable Ltd. Mettmenstetten Switzerland School of Physical & Mathematical Sciences Nanyang Technological University Singapore Nanyang Technological University Singapore Institute for Digital Molecular Analytics & Sciences Nanyang Technological University Singapore Department of Biomedical Engineering Texas A&M University United States Department of Electrical and Computer Engineering Texas A&M University United States Department of Physics and Astronomy Texas A&M University United States Harvard Me

Brillouin Light Scattering (BLS) spectroscopy is a non-invasive, non-contact, label-free optical technique that can provide information on the mechanical properties of a material on the sub-micron scale. Over the last decade it has seen increased applications in the life sciences, driven by the observed significance of mechanical properties in biological processes, the realization of more sensitive BLS spectrometers and its extension to an imaging modality. As with other spectroscopic techniques, BLS measurements not only detect signals characteristic of the investigated sample, but also of the experimental apparatus, and can be significantly affected by measurement conditions. The aim of this consensus statement is to improve the comparability of BLS studies by providing reporting recommendations for the measured parameters and detailing common artifacts. Given that most BLS studies of biological matter are still at proof-of-concept stages and use different-often self-built-spectrometers, a consensus statement is particularly timely to assure unified advancement. © 2024, CC BY.

关键词： Brillouin scattering

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Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

arXiv

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arXiv 2021年

作者： Giuste, Felipe Shi, Wenqi Zhu, Yuanda Naren, Tarun Isgut, Monica Sha, Ying Tong, Li Gupte, Mitali Wang, May D. the Wallace H. Coulter School of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA30322 United States the Department of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332 United States the School of Biology Georgia Institute of Technology AtlantaGA30322 United States the Department of Nuclear and Radiological Engineering Georgia Institute of Technology AtlantaGA30332 United States

Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during the pandemic, few have made significant clinical impact. The impact of artificial intelligence during the COVID-19 pandemic was greatly limited by lack of model transparency. This systematic review examines the use of Explainable Artificial Intelligence (XAI) during the pandemic and how its use could overcome barriers to real-world success. We find that successful use of XAI can improve model performance, instill trust in the end-user, and provide the value needed to affect user decision-making. We introduce the reader to common XAI techniques, their utility, and specific examples of their application. Evaluation of XAI results is also discussed as an important step to maximize the value of AI-based clinical decision support systems. We illustrate the classical, modern, and potential future trends of XAI to elucidate the evolution of novel XAI techniques. Finally, we provide a checklist of suggestions during the experimental design process supported by recent publications. Common challenges during the implementation of AI solutions are also addressed with specific examples of potential solutions. We hope this review may serve as a guide to improve the clinical impact of future AI-based solutions. Copyright © 2021, The Authors. All rights reserved.

关键词： COVID-19

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Novel Smart N95 Filtering Facepiece Respirator with Real-time Adaptive Fit Functionality and Wireless Humidity Monitoring for Enhanced Wearable Comfort

arXiv

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arXiv 2023年

作者： Kwon, Kangkyu Lee, Yoon Jae Jung, Yeongju Soltis, Ira Choi, Chanyeong Na, Yewon Romero, Lissette Kim, Myung Chul Rodeheaver, Nathan Kim, Hodam Lloyd, Michael S. Zhuang, Ziqing King, William Xu, Susan Ko, Seung-Hwan Lee, Jinwoo Yeo, Woon-Hong School of Electrical and Computer Engineering College of Engineering Georgia Institute of Technology AtlantaGA30332 United States Center for Human-Centric Interfaces and Nanoengineering Institute for Electronics and Nanotechnology Georgia Institute of Technology AtlantaGA30332 United States Applied Nano and Thermal Science Lab Department of Mechanical Engineering Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States School of Industrial Design Georgia Institute of Technology AtlantaGA30332 United States Division of Cardiology Emory University School of Medicine AtlantaGA30322 United States National Institute for Occupational Safety and Health National Personal Protective Technology Laboratory PittsburghPA15236 United States Seoul National University Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of Department of Mechanical Robotics and Energy Engineering Dongguk University 30 Pildongro 1-gil Jung-gu Seoul04620 Korea Republic of Wallace H. Coulter Department of Biomedical Engineering Parker H. Petit Institute for Bioengineering and Biosciences Neural Engineering Center Institute for Materials Institute for Robotics and Intelligent Machines Georgia Institute of Technology AtlantaGA30332 United States

The widespread emergence of the COVID-19 pandemic has transformed our lifestyle, and facial respirators have become an essential part of daily life. Nevertheless, the current respirators possess several limitations such as poor respirator fit because they are incapable of covering diverse human facial sizes and shapes, potentially diminishing the effect of wearing respirators. In addition, the current facial respirators do not inform the user of the air quality within the smart facepiece respirator in case of continuous long-term use. Here, we demonstrate the novel smart N-95 filtering facepiece respirator that incorporates the humidity sensor and pressure sensory feedback-enabled self-fit adjusting functionality for the effective performance of the facial respirator to prevent the transmission of airborne pathogens. The laser-induced graphene (LIG) constitutes the humidity sensor, and the pressure sensor array based on the dielectric elastomeric sponge monitors the respirator contact on the user’s face, providing the sensory information for a closed-loop feedback mechanism. As a result of the self-fit adjusting mode along with elastomeric lining, the fit factor is increased by 3.20 and 5 times at average and maximum respectively. We expect that the experimental proof-of-concept of this work will offer viable solutions to the current commercial respirators to address the *** Codes 92C55 Copyright © 2023, The Authors. All rights reserved.

关键词： Graphene

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Multiregion Multiscale Image Segmentation with Anisotropic Diffusion 25th

Multiregion Multiscale Image Segmentation with Anisotropic D...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Prasath, V. B. Surya Thanh, Dang Ngoc Hoang Hai, Nguyen Hoang Dvoenko, Sergey Department of Electrical Engineering and Computer Science University of Cincinnati CincinnatiOH45221 United States Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center CincinnatiOH45229 United States Department of Biomedical Informatics College of Medicine University of Cincinnati CincinnatiOH45267 United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH45257 United States Department of Information Technology School of Business Information Technology University of Economics Ho Chi Minh City Ho Chi Minh City Viet Nam Department of Computer Science Vietnam-Korea University of Information and Communication Technology – The University of Danang Danang Viet Nam Department of Information Security Institute of Applied Mathematics and Computer Science Tula State University Tula city Russia

ISBN: (纸本)9783030688202

We present a multiregion image segmentation approach which utilizes multiscale anisotropic diffusion based scale spaces. By combining powerful edge preserving anisotropic diffusion smoothing with isolevel set linking and merging, we obtain coherent segments which are tracked across multiple scales. A hierarchical tree representation of the given input image with progressively simplified regions is used with intra-scale splitting and inter-scale merging for obtaining multiregion segmentations. Experimental results on natural and medical images indicate that multiregion, multiscale image segmentation (MMIS) approach obtains coherent segmentation results. © 2021, Springer Nature Switzerland AG.

关键词： Diffusion

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A Wireless Near-Infrared Spectroscopy Device for Flap Monitoring: Proof of Concept in a Porcine Musculocutaneous Flap Model

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Journal of Reconstructive Microsurgery 2021年第2期38卷 096-105页

作者： Changsheng Wu Alina Y. Rwei Jong Yoon Lee Wei Ouyang Lauren Jacobson Haixu Shen Haiwen Luan Yameng Xu Jun Bin Park Sung Soo Kwak Xiaoyue Ni Wubin Bai Daniel Franklin Shuo Li Yiming Liu Xinchen Ni Amanda M. Westman Matthew R. MacEwan John A. Rogers Mitchell A. Pet Department of Materials Science and Engineering Querrey Simpson Institute for Bioelectronics Northwestern University Chicago Illinois Department of Chemical Engineering Delft University of Technology Delft The Netherlands Sibel Inc. Evanston Illinois Division of Plastic and Reconstructive Surgery Department of Surgery School of Medicine Washington University St. Louis Missouri Department of Materials Science and Engineering Northwestern University Evanston Illinois Department of Neurosurgery School of Medicine Washington University St. Louis Missouri Department of Electrical and Computer Engineering Northwestern University Evanston Illinois Department of Mechanical Engineering Northwestern University Evanston Illinois Department of Biomedical Engineering Northwestern University Evanston Illinois Department of Chemistry Northwestern University Evanston Illinois Department of Neurological Surgery Feinberg School of Medicine Northwestern University Chicago Illinois

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Guest Editorial Introduction to JSTQE Issue on Nanobiophotonics

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IEEE Journal of Selected Topics in Quantum Electronics 2021年第5期27卷

作者： Ilev, Ilko Armani, Andrea Tunnell, James Kim, Beop-Min Shih, Wei-Chuan Fales, Andrew Hildebrandt, Niko Center for Devices and Radiological Health U.S. Food and Drug Administration Silver SpringMD United States Department of Chemical Engineering and Materials Science University of Southern California Los AngelesCA United States Department of Biomedical Engineering The University of Texas at Austin AustinTX United States Department of Biomedical Engineering Korea University Seoul Korea Republic of Department of Electrical and Computer Engineering University of Houston HoustonTX United States Center for Devices and Radiological Health U.S. Food and Drug Administration Silver SpringMD United States Université Paris-Saclay and Université de Rouen Normandie Mont-Saint-Aignan France

The papers in this special section focus on nanobiophotonics. This is an emerging field of modern science and biomedical nanotechnology. This new field continues to vastly expand with state-of-the-art developments across the entire spectrum of biomedical applications ranging from fundamental studies of light-nanobiomaterial interactions to clinical diagnostics and therapeutics. In nanobiophotonics research areas, there has been a great impetus recently for the development of noninvasive imaging and sensing of intracellular structures and cellular functions as well as obtaining quantitative information for light-tissue interactions at the cellular, intracellular, and molecular level. Existing photonics technologies provide unique capabilities for investigating these scientific questions in terms of using non-ionizing optical radiation for providing noninvasive intracellular, cellular, and tissue imaging, and in-vivo sensing with ultrahigh resolution in the subwavelength nanoscale range (smaller than 100 nm). Furthermore, recent technology development efforts leverage the advanced features of photonics to move beyond the diffraction limit, providing sub-nanometer resolution into cellular processes.

关键词： Special issues and sections Nanobiophotonics Image resolution Optical sensors Optical imaging Optical diffraction Nanoscale devices Biosensors biomedical optical imaging

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Boxpect: High Sensitivity Multi-Pinhole Brain SPECT

Boxpect: High Sensitivity Multi-Pinhole Brain SPECT

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2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020

作者： Ozsahin, Ilker Ozsahin, Dilber Uzun Makarov, Pavel A. Mok, Greta S.P. The Department of Biomedical Engineering Faculty of Engineering DESAM Institute Near East University Turkish Republic of Northern Cyprus Nicosia Turkey Brain Health Imaging Institute Department of Radiology Weill Cornell Medicine New YorkNY United States Medical Diagnostic Imaging Department College of Health Sciences University of Sharjah Sharjah United Arab Emirates The Innovation and Information Technologies Center Near East University Turkish Republic of Northern Cyprus Nicosia Turkey The Biomedical Imaging Laboratory Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau China

ISBN: (纸本)9781728176932

Neurological diseases are currently one of the main causes of disability and mortality worldwide. Alzheimer's disease (AD) and Parkinson's disease (PD) are the two most common brain disorders with no cure. Neuroimaging based methods, such as magnetic resonance imaging (MRI), positron emission tomography (PET) and single photon emission computed tomography (SPECT) imaging, have been used to diagnose AD and PD. However, PET requires a cyclotron onsite for generally short-lived isotopes. In addition, PET and MRI are more expensive and less accessible. SPECT not only costs less than PET and MRI, but also has longer half-life isotopes available. The aim of this study is to evaluate a high sensitivity multi-pinhole brain SPECT system. The proposed system sensitivity was evaluated using Geant4 Application for Tomographic Emission. We first determined the design parameters by maximizing the sensitivity for a target resolution of 11.4 mm and field of view of 200 mm. The preliminary simulation results showed that a system sensitivity of 0.204% was achieved, which is very high compared to existing clinical brain SPECT systems. The improved sensitivity of the device can be traded for better spatial resolution, reduced injection dose or shortened acquisition time. © 2020 IEEE

关键词： Single photon emission computed tomography

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Light polarization interaction with skin conditions assessed by polarization sensitive optical coherence tomography

Light polarization interaction with skin conditions assessed...

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Novel Techniques in Microscopy, NTM 2021 - Part of Biophotonics Congress: Optics in the Life Sciences 2021

作者： Zhou, Xin Louie, Daniel C. Maloufia, Sina Tchvialeva, Lioudmila Lee, Tim K. Tang, Shuo Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada School of Biomedical Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Dermatology and Skin Science Photomedicine Institute University of British Columbia Vancouver Coastal Health Research Institute VancouverBCV5Z 4E8 Canada Cancer Control Research Program BC Cancer VancouverBCV5Z 1L3 Canada

Polarization sensitive optical coherence tomography (PS-OCT) is utilized to assess skin characterizations, including layered tissue structures, light depolarization, and tissue birefringence. The multiple contrasts fr... 详细信息

ISBN: (纸本)9781557528209

关键词： Optical tomography

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