Applying Transfer Learning in Classification of Ischemia from Myocardial Polar Maps in PET Cardiac Perfusion Imaging

Introduction: Ischemia is defined as the restriction of blood flow to a body organ, such as the heart, resulting in a cutback in oxygen supply. Myocardial ischemia is characterized by an imbalance between myocardial oxygen supply and demand, causing cardiac dysfunction, arrhythmia, myocardial infarction, and sudden death. Positron emission tomography myocardial perfusion imaging (PET-MPI) is an examination for accurately evaluating blood circulation to the heart muscle at stress and rest. Images obtained from this technique can be interpreted by experts or potentially classified by deep learning for the diagnosis of cardiac ischemia. Although deep learning has proved to be effective for medical image classification tasks, the challenge of small medical image datasets for model training remains to exist. Transfer learning is a state-of-the-art technique for resolving this challenge by utilizing pre-trained models for a new task. Pre-trained models are deep convolutional neural networks (CNNs) trained on a vast dataset, such as ImageNet, capable of transferring learned weights to a new classification problem. Objective: To study the effectiveness of image classification using transfer learning and benchmarking pre-trained CNN models for the classification of myocardial ischemia from myocardial polar maps in PET 15O-H2O cardiac perfusion imaging. Subject and methods: 138 JPEG polar maps from a 15O-H2O stress perfusion test from patients classified as ischemic or non-ischemic were used. Experiments for comparing a total of 20 pre-trained CNN models were performed. The results were compared against a custom CNN developed on the same dataset. Python programming language and its relevant libraries for deep learning were used. Results and discussion: Pre-trained models showed reliable performance compared to a custom-built CNN. VGG19, VGG16, DenseNet169, and Xception were superior among all pre-trained models. Ensemble learning improved overall performance, closest to the clinical interpretation level

Radiomics in prostate cancer: an up-to-date review

: Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications

Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases

Cardiothoracic and pulmonary diseases are a significant cause of mortality and morbidity worldwide. The COVID-19 pandemic has highlighted the lack of access to clinical care, the overburdened medical system, and the potential of artificial intelligence (AI) in improving medicine. There are a variety of diseases affecting the cardiopulmonary system including lung cancers, heart disease, tuberculosis (TB), etc., in addition to COVID-19-related diseases. Screening, diagnosis, and management of cardiopulmonary diseases has become difficult owing to the limited availability of diagnostic tools and experts, particularly in resource-limited regions. Early screening, accurate diagnosis and staging of these diseases could play a crucial role in treatment and care, and potentially aid in reducing mortality. Radiographic imaging methods such as computed tomography (CT), chest X-rays (CXRs), and echo ultrasound (US) are widely used in screening and diagnosis. Research on using image-based AI and machine learning (ML) methods can help in rapid assessment, serve as surrogates for expert assessment, and reduce variability in human performance. In this Special Issue, “Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases”, we have highlighted exemplary primary research studies and literature reviews focusing on novel AI/ML methods and their application in image-based screening, diagnosis, and clinical management of cardiopulmonary diseases. We hope that these articles will help establish the advancements in AI

Is attention all you need in medical image analysis? A review

Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance gains in medical image analysis (MIA) over the last years. CNNs can efficiently model local pixel interactions and be trained on small-scale MI data. The main disadvantage of typical CNN models is that they ignore global pixel relationships within images, which limits their generalisation ability to understand out-of-distribution data with different 'global' information. The recent progress of Artificial Intelligence gave rise to Transformers, which can learn global relationships from data. However, full Transformer models need to be trained on large-scale data and involve tremendous computational complexity. Attention and Transformer compartments (Transf/Attention) which can well maintain properties for modelling global relationships, have been proposed as lighter alternatives of full Transformers. Recently, there is an increasing trend to co-pollinate complementary local-global properties from CNN and Transf/Attention architectures, which led to a new era of hybrid models. The past years have witnessed substantial growth in hybrid CNN-Transf/Attention models across diverse MIA problems. In this systematic review, we survey existing hybrid CNN-Transf/Attention models, review and unravel key architectural designs, analyse breakthroughs, and evaluate current and future opportunities as well as challenges. We also introduced a comprehensive analysis framework on generalisation opportunities of scientific and clinical impact, based on which new data-driven domain generalisation and adaptation methods can be stimulated