AIMC Topic: Image Interpretation, Computer-Assisted
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Predicting Overall Survival of Glioblastoma Patients Using Deep Learning Classification Based on MRIs.
Studies in health technology and informatics
Aug 30, 2024
INTRODUCTION: Glioblastoma (GB) is one of the most aggressive tumors of the brain. Despite intensive treatment, the average overall survival (OS) is 15-18 months. Therefore, it is helpful to be able to assess a patient's OS to tailor treatment more s...
Automatic Classification of Wound Images Showing Healing Complications: Towards an Optimised Approach for Detecting Maceration.
Studies in health technology and informatics
Aug 30, 2024
This study aims to advance the field of digital wound care by developing and evaluating convolutional neural network (CNN) architectures for the automatic classification of maceration, a significant wound healing complication, in 458 annotated wound ...
Explaining Deep Learning Models Applied in Histopathology: Current Developments and the Path to Sustainability.
Studies in health technology and informatics
Aug 22, 2024
The digital pathology landscape is in continuous expansion. The digitalization of slides using WSIs (Whole Slide Images) fueled the capacity of automatic support for diagnostics. The paper presents an overview of the current state of the art methods ...
Automatic Tumor Cellularity Measurement: AI-Based Pipeline for Multi-Organ Pathology Imaging.
Studies in health technology and informatics
Aug 22, 2024
Tumor Cellularity (TC) is an important metric for assessing organ tumor burden. However, manual cell counting is not feasible due to large volumes of pathology images and inconsistent measurements between pathologists. The PAIP 2023 Challenge aimed t...
Comparative Analysis of Artificial Intelligence Algorithms for Breast Cancer Detection from Pathological Image in Burkina Faso.
Studies in health technology and informatics
Aug 22, 2024
Artificial Intelligence (AI) has revolutionized many fields, including medical imaging. This revolution has enabled the digitization of medical images, the development of algorithms allowing the use of data captured in natural language, and deep lear...
Artificial Intelligence System for Automated Breast Cancer Detection in Pathology in Burkina Faso: Methodology Overview.
Studies in health technology and informatics
Aug 22, 2024
The introduction of artificial intelligence (AI) in breast cancer diagnosis in Burkina Faso represents a significant advancement in the field of healthcare. Faced with the public health issue posed by breast cancer, this study focuses on the use of A...
Deep Learning-Based Synthetic Skin Lesion Image Classification.
Studies in health technology and informatics
Aug 22, 2024
Advances in general-purpose computers have enabled the generation of high-quality synthetic medical images that human eyes cannot differ between real and AI-generated images. To analyse the efficacy of the generated medical images, this study propose...
Deep Learning Based Automatic Fibroglandular Tissue Segmentation in Breast Magnetic Resonance Imaging Screening.
Studies in health technology and informatics
Aug 22, 2024
In light of the global increase in breast cancer cases and the crucial importance of the density of fibroglandular tissue (FGT) in assessing risk and predicting the course of the disease, the accurate measurement of FGT emerges as a significant chall...
Assessing the Performance of Deep Learning for Automated Gleason Grading in Prostate Cancer.
Studies in health technology and informatics
Aug 22, 2024
Prostate cancer is a dominant health concern calling for advanced diagnostic tools. Utilizing digital pathology and artificial intelligence, this study explores the potential of 11 deep neural network architectures for automated Gleason grading in pr...
Precise Identification of Oral Cancer Lesions Using Artificial Intelligence.
Studies in health technology and informatics
Aug 22, 2024
Dentists, especially those who are not oral lesion specialists and live in rural areas, need an artificial intelligence (AI) system for accurately assisting them in screening for oral cancer that may appear in smartphone images. Not many literatures ...
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