61 research outputs found
Intraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma
This paper describes the process of developing a classification model for the effective detection of malignant melanoma, an aggressive type of cancer in skin lesions. Primary focus is given on fine-tuning and improving a state-of-the-art convolutional neural network (CNN) to obtain the optimal ROC-AUC score. The study investigates a variety of artificial intelligence (AI) clustering techniques to train the developed models on a combined dataset of images across data from the 2019 and 2020 IIM-ISIC Melanoma Classification Challenges. The models were evaluated using varying cross-fold validations, with the highest ROC-AUC reaching a score of 99.48%
Intraclass clustering-based CNN approach for detection of malignant melanoma
This paper describes the process of developing a classification model for the effective detection of malignant melanoma, an aggressive type of cancer in skin lesions. Primary focus is given on fine-tuning and improving a state-of-the-art convolutional neural network (CNN) to obtain the optimal ROC-AUC score. The study investigates a variety of artificial intelligence (AI) clustering techniques to train the developed models on a combined dataset of images across data from the 2019 and 2020 IIM-ISIC Melanoma Classification Challenges. The models were evaluated using varying cross-fold validations, with the highest ROC-AUC reaching a score of 99.48%
Leveraging Computer Vision for Applications in Biomedicine and Geoscience
Skin cancer is one of the most common types of cancer and is usually classified as either non-melanoma and melanoma skin cancer. Melanoma skin cancer accounts for about half of all skin cancer-related deaths. The 5-year survival rate is 99% when the cancer is detected early but drops to 25% once it becomes metastatic. In other words, the key to preventing death is early detection.
Foraminifera are microscopic single-celled organisms that exist in marine environments and are classified as living a benthic or planktic lifestyle. In total, roughly 50,000 species are known to have existed, of which about 9,000 are still living today. Foraminifera are important proxies for reconstructing past ocean and climate conditions and as bio-indicators of anthropogenic pollution. Since the 1800s, the identification and counting of foraminifera have been performed manually. The process is resource-intensive.
In this dissertation, we leverage recent advances in computer vision, driven by breakthroughs in deep learning methodologies and scale-space theory, to make progress towards both early detection of melanoma skin cancer and automation of the identification and counting of microscopic foraminifera.
First, we investigate the use of hyperspectral images in skin cancer detection by performing a critical review of relevant, peer-reviewed research. Second, we present a novel scale-space methodology for detecting changes in hyperspectral images. Third, we develop a deep learning model for classifying microscopic foraminifera. Finally, we present a deep learning model for instance segmentation of microscopic foraminifera.
The works presented in this dissertation are valuable contributions in the fields of biomedicine and geoscience, more specifically, towards the challenges of early detection of melanoma skin cancer and automation of the identification, counting, and picking of microscopic foraminifera
Deep Learning Algorithm for Advanced Level-3 Inverse-Modeling of Silicon-Carbide Power MOSFET Devices
Inverse modelling with deep learning algorithms involves training deep
architecture to predict device's parameters from its static behaviour. Inverse
device modelling is suitable to reconstruct drifted physical parameters of
devices temporally degraded or to retrieve physical configuration. There are
many variables that can influence the performance of an inverse modelling
method. In this work the authors propose a deep learning method trained for
retrieving physical parameters of Level-3 model of Power Silicon-Carbide MOSFET
(SiC Power MOS). The SiC devices are used in applications where classical
silicon devices failed due to high-temperature or high switching capability.
The key application of SiC power devices is in the automotive field (i.e. in
the field of electrical vehicles). Due to physiological degradation or
high-stressing environment, SiC Power MOS shows a significant drift of physical
parameters which can be monitored by using inverse modelling. The aim of this
work is to provide a possible deep learning-based solution for retrieving
physical parameters of the SiC Power MOSFET. Preliminary results based on the
retrieving of channel length of the device are reported. Channel length of
power MOSFET is a key parameter involved in the static and dynamic behaviour of
the device. The experimental results reported in this work confirmed the
effectiveness of a multi-layer perceptron designed to retrieve this parameter.Comment: 13 pages, 8 figures, to be published on Journal of Physics:
Conference Serie
Biomedical Sensing and Imaging
This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor
White Paper 4: Challenges In Biomedicine & Health
Publicado en Madrid, 231 p. ; 17 cm.A lesson that we have learned from the pandemia caused by coronavirus is that solutions in health require coordinated actions. Beside this and other emerging and re-emerging infectious diseases, millions of Europeans are suffering a plethora of disorders that are currently acquiring epidemic dimensions, including cancer, rare diseases, pain and food allergies, among others. New tools for prevention, diagnosis and treatment need to be urgently designed and implemented using new holistic and multidisciplinary approaches at three different levels (basic research, translational/clinical and public/social levels) and involving researchers, clinicians, industry and all stakeholders in the health system. The CSIC is excellently positioned to lead and coordinate these challenges in Biomedicine and Health.Peer reviewe
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