Chest X-ray imaging has become increasingly crucial for diagnosing various medical conditions, including pneumonia, lung cancer, and heart diseases. Despite the growing number of chest X-ray images, their interpretation remains a manual and time-consuming process, often leading to radiologist burnout and delays in diagnosis. The integration of domain knowledge and deep learning techniques has the potential to improve diagnosis, classification, and localization of abnormalities in chest X-rays, while also addressing the challenge of model interpretability. This work proposes a series of novel methods combining radiomics features and deep learning techniques for chest X-ray diagnosis, classification, and localization. We first introduce a framework leveraging radiomics features and contrastive learning for pneumonia detection, achieving superior performance and interpretability. The second method, ChexRadiNet, utilizes radiomics features and a lightweight triplet-attention mechanism for enhanced abnormality classification performance. In addition, we present a semi-supervised knowledge-augmented contrastive learning framework that seamlessly integrates radiomic features as a knowledge augmentation for disease classification and localization. This approach leverages Grad-CAM to highlight crucial abnormal regions, extracting radiomic features that act as positive samples for image features generated from the same chest X-ray. Consequently, this framework creates a feedback loop, enabling image and radiomic features to mutually reinforce each other, resulting in robust and interpretable knowledge-augmented representations. The Radiomics-Guided Transformer (RGT) fuses global image information with local radiomics-guided auxiliary information for accurate cardiopulmonary pathology localization and classification without bounding box annotations. Experimental results on public datasets such as NIH ChestX-ray, CheXpert, MIMIC-CXR, and the RSNA Pneumonia Detection Challenge demonstrate the effectiveness of our proposed methods, consistently outperforming state-of-the-art models in chest X-ray diagnosis, classification, and localization tasks. By bridging the gap between traditional radiomics and deep learning approaches, this work aims to advance the field of medical image analysis and facilitate more efficient and accurate diagnoses in clinical practice

Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. Provides an authoritative description of key concepts and methods Includes tutorial-based sections that clearly explain principles and their application to different medical domains Presents a representative selection of topics to match a modern and relevant approach to medical image computing

This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammograms and glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.

This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.

Image registration is the process of systematically placing separate images in a common frame of reference so that the information they contain can be optimally integrated or compared. This is becoming the central tool for image analysis, understanding, and visualization in both medical and scientific applications. Medical Image Registration provid

Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis. · Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache

This book constitutes the refereed joint proceedings of the 4th International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2018, and the 8th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 39 full papers presented at DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were carefully reviewed and selected from 85 submissions to DLMIA and 6 submissions to ML-CDS. The DLMIA papers focus on the design and use of deep learning methods in medical imaging. The ML-CDS papers discuss new techniques of multimodal mining/retrieval and their use in clinical decision support.