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Search results for: medical image annotation
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Medical Image Dataset Annotation Service (MIDAS)
PublicationMIDAS (Medical Image Dataset Annotation Service) is a custom-tailored tool for creating and managing datasets either for deep learning, as well as machine learning or any form of statistical research. The aim of the project is to provide one-fit-all platform for creating medical image datasets that could easily blend in hospital's workflow. In our work, we focus on the importance of medical data anonimization, discussing the...
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Image simulation and annotation for color blinded
PublicationIn this paper methods for image simulation as seen by a color blinded and a method for constructing images of perceived color difference are presented. The work is also focused on the interactive color description of an image contents. As a result, the individuals having problems with color discrimination can identify colors in an image.W artykule prezentowane są metody symulacji kolorów w obrazach postrzeganych przez osoby ze...
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Image simulation and annotation for color blinded
PublicationW pracy przedstawiono metody symulacji obrazów widzianych przez osoby ze ślepota barw. Ukazano również metody tworzenia obrazów ukazujących różnicę w percepcji kolorów pomiędzy normalnym obserwatorem a osobą ze ślepotą barw. W artykule opisano również metodę interaktywnego opisu koloru wskazywanego piksela obrazu. W rezultacie użytkownik ze ślepota barw może uzyskać informacje opisowe o występujących w obrazie kolorach.
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Dependable Integration of Medical Image Recognition Components
PublicationComputer driven medical image recognition may support medical doctors in the diagnosis process, but requires high dependability considering potential consequences of incorrect results. The paper presentsa system that improves dependability of medical image recognition by integration of results from redundant components. The components implement alternative recognition algorithms of diseases in thefield of gastrointestinal endoscopy....
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Active Annotation in Evaluating the Credibility of Web-Based Medical Information: Guidelines for Creating Training Data Sets for Machine Learning
PublicationMethods Results Discussion References Abbreviations Copyright Abstract Background: The spread of false medical information on the web is rapidly accelerating. Establishing the credibility of web-based medical information has become a pressing necessity. Machine learning offers a solution that, when properly deployed, can be an effective tool in fighting medical misinformation on the web. Objective: The aim of this study is to...
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ColorNephroNet: Kidney tumor malignancy prediction using medical image colorization
PublicationRenal tumor malignancy classification is one of the crucial tasks in urology, being a primary factor included in the decision of whether to perform kidney removal surgery (nephrectomy) or not. Currently, tumor malignancy prediction is determined by the radiological diagnosis based on computed tomography (CT) images. However, it is estimated that up to 16% of nephrectomies could have been avoided because the tumor that had been...
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High Quality Medical Image-Guides By Mosaic-Assembling Optical Fibre Technology
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Segmentation Quality Refinement in Large-Scale Medical Image Dataset with Crowd-Sourced Annotations
PublicationDeployment of different techniques of deep learning including Convolutional Neural Networks (CNN) in image classification systems has accomplished outstanding results. However, the advantages and potential impact of such a system can be completely negated if it does not reach a target accuracy. To achieve high classification accuracy with low variance in medical image classification system, there is needed the large size of the...
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Medical Image Segmentation Using Deep Semantic-based Methods: A Review of Techniques, Applications and Emerging Trends
PublicationSemantic-based segmentation (Semseg) methods play an essential part in medical imaging analysis to improve the diagnostic process. In Semseg technique, every pixel of an image is classified into an instance, where each class is corresponded by an instance. In particular, the semantic segmentation can be used by many medical experts in the domain of radiology, ophthalmologists, dermatologist, and image-guided radiotherapy. The authors...
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MEDICAL IMAGE ANALYSIS
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Medical Image Computing and Computer-Assisted Intervention
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Instrument detection and pose estimation with rigid part mixtures model in video-assisted surgeries
PublicationLocalizing instrument parts in video-assisted surgeries is an attractive and open computer vision problem. A working algorithm would immediately find applications in computer-aided interventions in the operating theater. Knowing the location of tool parts could help virtually augment visual faculty of surgeons, assess skills of novice surgeons, and increase autonomy of surgical robots. A surgical tool varies in appearance due to...
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Sequelae of surgical and medical procedures as the cause of abnormal reaction of the patient, or of later complication, without mention of misadventure at the time of the procedure - Male, 49 - Tissue image [6260730021886091]
Open Research DataThis is the histopathological image of CONNECTIVE, SUBCUTANEOUS AND OTHER SOFT TISSUES tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
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Sequelae of surgical and medical procedures as the cause of abnormal reaction of the patient, or of later complication, without mention of misadventure at the time of the procedure - Male, 49 - Tissue image [6260730021885141]
Open Research DataThis is the histopathological image of CONNECTIVE, SUBCUTANEOUS AND OTHER SOFT TISSUES tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
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Sequelae of surgical and medical procedures as the cause of abnormal reaction of the patient, or of later complication, without mention of misadventure at the time of the procedure - Male, 49 - Tissue image [6260730021884451]
Open Research DataThis is the histopathological image of CONNECTIVE, SUBCUTANEOUS AND OTHER SOFT TISSUES tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
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Sequelae of surgical and medical procedures as the cause of abnormal reaction of the patient, or of later complication, without mention of misadventure at the time of the procedure - Male, 49 - Tissue image [6260730021884251]
Open Research DataThis is the histopathological image of CONNECTIVE, SUBCUTANEOUS AND OTHER SOFT TISSUES tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
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Sequelae of surgical and medical procedures as the cause of abnormal reaction of the patient, or of later complication, without mention of misadventure at the time of the procedure - Male, 49 - Tissue image [6260730021882191]
Open Research DataThis is the histopathological image of CONNECTIVE, SUBCUTANEOUS AND OTHER SOFT TISSUES tissue sample obtained in Medical University Gdańsk and deposited in ZMDL-GUMED. The sample image was taken using: Pannoramic 250 3DHistech slide scanner (20x magnification) and saved to DICOM format.
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Mariusz Kaczmarek dr hab. inż.
PeopleReceived M.Sc., Eng. in Electronics in 1995 from Gdansk University of Technology, Ph.D. in Medical Electronics in 2003 and habilitation in Biocybernetics and Biomedical Engineering in 2017. He was an investigator in about 13 projects receiving a number of awards, including four best papers, practical innovations (7 medals and awards) and also the Andronicos G. Kantsios Award and Siemens Award. Main research activities: the issues...
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Improving medical experts’ efficiency of misinformation detection: an exploratory study
PublicationFighting medical disinformation in the era of the pandemic is an increasingly important problem. Today, automatic systems for assessing the credibility of medical information do not offer sufficient precision, so human supervision and the involvement of medical expert annotators are required. Our work aims to optimize the utilization of medical experts’ time. We also equip them with tools for semi-automatic initial verification...
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Focus on Misinformation: Improving Medical Experts’ Efficiency of Misinformation Detection
PublicationFighting medical disinformation in the era of the global pandemic is an increasingly important problem. As of today, automatic systems for assessing the credibility of medical information do not offer sufficient precision to be used without human supervision, and the involvement of medical expert annotators is required. Thus, our work aims to optimize the utilization of medical experts’ time. We use the dataset of sentences taken...
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Semantic segmentation training using imperfect annotations and loss masking
PublicationOne of the most significant factors affecting supervised neural network training is the precision of the annotations. Also, in a case of expert group, the problem of inconsistent data annotations is an integral part of real-world supervised learning processes, well-known to researchers. One practical example is a weak ground truth delineation for medical image segmentation. In this paper, we have developed a new method of accurate...
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THE ROLE OF INFERENCE IN MOBILE MEDICAL APPLICATION DESIGN
PublicationIn the early 21st century, artificial intelligence began to be used to process medical information. However, before this happened, predictive models used in healthcare could only consider a limited number of variables, and only in properly structured and organised medical data. Today, advanced tools based on machine learning techniques - which, using artificial neural networks, can explore extremely complex relationships - and...
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The parallel environment for endoscopic image analysis
PublicationThe jPVM-oriented environment to support high performance computing required for the Endoscopy Recommender System (ERS) is defined. SPMD model of image matching is considered and its two implementations are proposed: Lexicographical Searching Algorithm (LSA) and Gradient Serching Algorithm (GSA). Three classes of experiments are considered and the relative degree of similarity and execution time of each algorithm are analysed....
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Art and Healthcare - Healing Potential of Artistic Interventions in Medical Settings
PublicationThe stereotype of a machine for healing seems to be well rooted in common thinking and social perception of hospital buildings. The technological aspect of healthcare architecture has been influenced for several years by three major factors. The first is linked to the necessity of providing safety and security in the environment of elevated epidemiological risk. The second concerns the need for incorporating advanced technology...
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Thermal Images Analysis Methods using Deep Learning Techniques for the Needs of Remote Medical Diagnostics
PublicationRemote medical diagnostic solutions have recently gained more importance due to global demographic shifts and play a key role in evaluation of health status during epidemic. Contactless estimation of vital signs with image processing techniques is especially important since it allows for obtaining health status without the use of additional sensors. Thermography enables us to reveal additional details, imperceptible in images acquired...
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Assessment Of the Relevance of Best Practices in The Development of Medical R&D Projects Based on Machine Learning
PublicationMachine learning has emerged as a fundamental tool for numerous endeavors within health informatics, bioinformatics, and medicine. However, novices among biomedical researchers and IT developers frequently lack the requisite experience to effectively execute a machine learning project, thereby increasing the likelihood of adopting erroneous practices that may result in common pitfalls or overly optimistic predictions. The paper...
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BIG DATA SIGNIFICANCE IN REMOTE MEDICAL DIAGNOSTICS BASED ON DEEP LEARNING TECHNIQUES
PublicationIn this paper we discuss the evaluation of neural networks in accordance with medical image classification and analysis. We also summarize the existing databases with images which could be used for training deep models that can be later utilized in remote home-based health care systems. In particular, we propose methods for remote video-based estimation of patient vital signs and other health-related parameters. Additionally, potential...
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Data augmentation for improving deep learning in image classification problem
PublicationThese days deep learning is the fastest-growing field in the field of Machine Learning (ML) and Deep Neural Networks (DNN). Among many of DNN structures, the Convolutional Neural Networks (CNN) are currently the main tool used for the image analysis and classification purposes. Although great achievements and perspectives, deep neural networks and accompanying learning algorithms have some relevant challenges to tackle. In this...
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The Use of Liquid Crystal Thermography in Selected Technical and Medical Applications—Recent Development
PublicationThermochromic liquid crystals (TLC) and true-colour digital image processing have been successfully used in non-intrusive technical, industrial and biomedical studies and applications. Thin coatings of TLC at surfaces are utilized to obtain detailed temperature distributions and heat transfer rates for steady or transient processes. Liquid crystals also can be used to make the temperature and velocity fields in liquids visible...
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Analogue CMOS ASICs in Image Processing Systems
PublicationIn this paper a survey of analog application specific integrated circuits (ASICs) for low-level image processing, called vision chips, is presented. Due to the specific requirements, the vision chips are designed using different architectures best suited to their functions. The main types of the vision chip architectures and their properties are presented and characterized on selected examples of prototype integrated circuits (ICs)...
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UPDRS tests for diagnosis of Parkinson's disease employing virtual-touchpad
PublicationThis paper presents a new approach to diagnosing Parkinson's disease. The progression of the disease can be measured by the UPDRS (Unified Parkinson Disease Rating Scale) scale which is used to evaluate motor and behavioral symptoms of Parkinson's disease. Hitherto the evaluation of the advancement of the disease in the UPDRS scale was made by a specialist through medical observation. The authors suggest a partial automation of...
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Impact of Visual Image Quality on Lymphocyte Detection Using YOLOv5 and RetinaNet Algorithms
PublicationLymphocytes, a type of leukocytes, play a vital role in the immune system. The precise quantification, spatial arrangement and phenotypic characterization of lymphocytes within haematological or histopathological images can serve as a diagnostic indicator of a particular lesion. Artificial neural networks, employed for the detection of lymphocytes, not only can provide support to the work of histopathologists but also enable better...
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Non invasive optical cellular imaging in humans.
PublicationOne of the most appealing and still unsolved problems in biological and medical imaging is the possibility of noninvasive visualization of tissue in vivo with an accuracy of microscopic examination. A major difficulty to solve in biomedical imaging is a degradation of image quality caused by the presence of optical inhomogeneity of tissue. Is there any chance to develop a microscopic method that allows non-invasive observation...
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Trustworthy Applications of ML Algorithms in Medicine - Discussion and Preliminary Results for a Problem of Small Vessels Disease Diagnosis.
PublicationML algorithms are very effective tools for medical data analyzing, especially at image recognition. Although they cannot be considered as a stand-alone diagnostic tool, because it is a black-box, it can certainly be a medical support that minimize negative effect of human-factors. In high-risk domains, not only the correct diagnosis is important, but also the reasoning behind it. Therefore, it is important to focus on trustworthiness...
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OntoValidate: OntoNotes 5.0 NER validation dataset
Open Research DataOntoValidate dataset consists of 603 randomly chosen raw textsfrom the original OntoNote 5.0 dataset (3637 raw texts in total).
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Digital Photogrammetry in the Analysis of the Ventricles' Shape and Size
PublicationThis article presents spatial analyzes conducted to assess the potential of ReMake software to be used for medical purposes, with emphasis on the analysis of the shape and dimensions of the ventricles. To achieve this goal, the length of the sections measured with the ReMake and Image Master programs have been compared. RMS error was on the level of 1.2 mm. In addition to indicating the appropriateness of using this software, there...
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Using Eye-tracking to get information on the skills acquisition by the radiology residents
PublicationThis paper describes the possibility of monitoring the progress of knowledge and skills acquisition by the students of radiology. It is achieved by an analysis of a visual attention distribution patterns during image-based tasks solving. The concept is to use the eye-tracking data to recognize the way how the radiographic images are read by recognized experts, radiography residents involved in the training program, and untrained...
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Rust QA: question answering dataset for "The Rust Programming Language" in SQuAD 2.0 format
Open Research DataRust QA is a dataset for training and evaluating QA systems. The dataset consists of 1068 questions to "The Rust Programming Language" book (https://doc.rust-lang.org/stable/book/) with the answers provided as text spans from the book. The dataset is released in SQuAD 2.0 format.
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Assessment of particular abdominal aorta section extraction from contrast-enhanced computed tomography angiography
PublicationThe aim of this work is to improve the accuracy of extraction of a particular abdominal aorta section and to reduce the distortion in three-dimensional Computed Tomography Angiography (CTA) images. Imaging modality and quality plays crucial role in the medical diagnostic process, thus ensuring high quality of images is essential at every stage of acquisition and processing.Noise is defined as a disturbance of the image quality...
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Evaluation of Respiration Rate Using Thermal Imaging in Mobile Conditions
PublicationRespiratory rate is very important vital sign that should be measured and documented in many medical situations. The remote measurement of respiration rate can be especially valuable for medical screening purposes (e.g. severe acute respiratory syndrome (SARS), pandemic influenza, etc.). In this chapter we present a review of many different studies focused on the measurements and estimation of respiration rate using thermal imaging...
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A Mammography Data Management Application for Federated Learning
PublicationThis study aimed to develop and assess an application designed to enhance the management of a local client database consisting of mammographic images with a focus on ensuring that images are suitably and uniformly prepared for federated learning applications. The application supports a comprehensive approach, starting with a versatile image-loading function that supports DICOM files from various medical imaging devices and settings....
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Redefiniowanie przestrzeni medycznej = Redefining healthcare space
PublicationSzpital jest obiektem publicznym, budynkiem-miastem, jego architektura nakierowana jest na realizację procesu leczenia i zdrowienia, a jednocześnie formuje przestrzenne ramy mikrokosmosu interakcji społecznych rozgrywających się pomiędzy pacjentami i personelem, gośćmi i „mieszkańcami”. Współcześnie w podejściu do rozumienia czym jest szpital - a zatem również do kształtowania architektury obiektów medycznych - można zauważyć dwa...
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Parallelization of video stream algorithms in kaskada platform
PublicationThe purpose of this work is to present different techniques of video stream algorithms parallelization provided by the Kaskada platform - a novel system working in a supercomputer environment designated for multimedia streams processing. Considered parallelization methods include frame-level concurrency, multithreading and pipeline processing. Execution performance was measured on four time-consuming image recognition algorithms,...
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Deep Learning-Based Cellular Nuclei Segmentation Using Transformer Model
PublicationAccurate segmentation of cellular nuclei is imperative for various biological and medical applications, such as cancer diagnosis and drug discovery. Histopathology, a discipline employing microscopic examination of bodily tissues, serves as a cornerstone for cancer diagnosis. Nonetheless, the conventional histopathological diagnosis process is frequently marred by time constraints and potential inaccuracies. Consequently, there...
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An Overview of the Development of a Real-Time System for Endoscopic Video Classification
PublicationThe article presents the results of improving endoscopic image classification algorithms in an effort towards applying them in a real-time diagnosis supporting system. Methods for the detection and removal of personal data are presented and discussed. The currently developed recognition algorithms have been improved in terms of accuracy and performance to make them suitable for a real-life implementation. Their test results are...
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Real-Time Facial Features Detection from Low Resolution Thermal Images with Deep Classification Models
PublicationDeep networks have already shown a spectacular success for object classification and detection for various applications from everyday use cases to advanced medical problems. The main advantage of the classification models over the detection models is less time and effort needed for dataset preparation, because classification networks do not require bounding box annotations, but labels at the image level only. Yet, after passing...
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Explainable AI for Inspecting Adversarial Attacks on Deep Neural Networks
PublicationDeep Neural Networks (DNN) are state of the art algorithms for image classification. Although significant achievements and perspectives, deep neural networks and accompanying learning algorithms have some important challenges to tackle. However, it appears that it is relatively easy to attack and fool with well-designed input samples called adversarial examples. Adversarial perturba-tions are unnoticeable for humans. Such attacks...
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Elektroniczne instrumenty komunikacji marketingowej w marketingu usług medycznych
PublicationNie ma wątpliwości, że wielu polskich świadczeniodawców usług zdrowotnych korzysta z Internetu do komunikowania się z pacjentami. Jednocześnie chcą oni stworzyć wizerunek marki jako bardziej nowoczesnej i atrakcyjnej. Elektroniczna komunikacja marketingowa (szczególnie serwisy informacyjne, serwisy społecznościowe, blogi, fora, microblogi, wyszukiwarki, marketing mobilny) ma coraz większe znaczenie w marketingu usług medycznych,...
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Template chart detection for stoma telediagnosis
PublicationThe paper presents the concept of using color template charts for the needs of telemedicine, particularly telediagnosis of the stoma. Although the concept is not new, the current popularity and level of development of digital cameras, especially those embedded in smartphones, allow common and reliable remote advice on various medical problems, which can be very important in the case of limitations in a physical contact with a doctor....
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Tomasz Gośliński prof. dr hab.
PeopleProfessor Tomasz Goslinski is employed at Poznan University of Medical Sciences. He obtained his MSc degree from Poznan Medical Academy in 1997 and PhD degree from Institute of Bioorganic Chemistry of the Polish Academy of Sciences in 2003. In 2004 he received a 2-year Marie Curie EIF Fellowship to join as a Post-Doctoral Research Fellow the group of Professor Anthony G.M. Barrett at Imperial College London, United Kingdom. As...