A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.

Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper's main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic

Exploring variability in medical imaging

Although recent successes of deep learning and novel machine learning techniques improved the perfor- mance of classification and (anomaly) detection in computer vision problems, the application of these methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this is the amount of variability that is encountered and encapsulated in human anatomy and subsequently reflected in medical images. This fundamental factor impacts most stages in modern medical imaging processing pipelines. Variability of human anatomy makes it virtually impossible to build large datasets for each disease with labels and annotation for fully supervised machine learning. An efficient way to cope with this is to try and learn only from normal samples. Such data is much easier to collect. A case study of such an automatic anomaly detection system based on normative learning is presented in this work. We present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative models, which are trained only utilising normal/healthy subjects. However, despite the significant improvement in automatic abnormality detection systems, clinical routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis and localise abnormalities. Integrating human expert knowledge into the medical imaging processing pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per- spective of building an automated medical imaging system, it is still an open issue, to what extent this kind of variability and the resulting uncertainty are introduced during the training of a model and how it affects the final performance of the task. Consequently, it is very important to explore the effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as on the model’s performance in a specific machine learning task. A thorough investigation of this issue is presented in this work by leveraging automated estimates for machine learning model uncertainty, inter-observer variability and segmentation task performance in lung CT scan images. Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging was attempted. This state-of-the-art survey includes both conventional pattern recognition methods and deep learning based methods. It is one of the first literature surveys attempted in the specific research area.Open Acces

Integrated Graph Theoretic, Radiomics, and Deep Learning Framework for Personalized Clinical Diagnosis, Prognosis, and Treatment Response Assessment of Body Tumors

Purpose: A new paradigm is beginning to emerge in radiology with the advent of increased computational capabilities and algorithms. The future of radiological reading rooms is heading towards a unique collaboration between computer scientists and radiologists. The goal of computational radiology is to probe the underlying tissue using advanced algorithms and imaging parameters and produce a personalized diagnosis that can be correlated to pathology. This thesis presents a complete computational radiology framework (I GRAD) for personalized clinical diagnosis, prognosis and treatment planning using an integration of graph theory, radiomics, and deep learning. Methods: There are three major components of the I GRAD framework–image segmentation, feature extraction, and clinical decision support. Image Segmentation: I developed the multiparametric deep learning (MPDL) tissue signature model for segmentation of normal and abnormal tissue from multiparametric (mp) radiological images. The segmentation MPDL network was constructed from stacked sparse autoencoders (SSAE) with five hidden layers. The MPDL network parameters were optimized using k-fold cross-validation. In addition, the MPDL segmentation network was tested on an independent dataset. Feature Extraction: I developed the radiomic feature mapping (RFM) and contribution scattergram (CSg) methods for characterization of spatial and inter-parametric relationships in multiparametric imaging datasets. The radiomic feature maps were created by filtering radiological images with first and second order statistical texture filters followed by the development of standardized features for radiological correlation to biology and clinical decision support. The contribution scattergram was constructed to visualize and understand the inter-parametric relationships of the breast MRI as a complex network. This multiparametric imaging complex network was modeled using manifold learning and evaluated using graph theoretic analysis. Feature Integration: The different clinical and radiological features extracted from multiparametric radiological images and clinical records were integrated using a hybrid multiview manifold learning technique termed the Informatics Radiomics Integration System (IRIS). IRIS uses hierarchical clustering in combination with manifold learning to visualize the high-dimensional patient space on a two-dimensional heatmap. The heatmap highlights the similarity and dissimilarity between different patients and variables. Results: All the algorithms and techniques presented in this dissertation were developed and validated using breast cancer as a model for diagnosis and prognosis using multiparametric breast magnetic resonance imaging (MRI). The deep learning MPDL method demonstrated excellent dice similarity of 0.87±0.05 and 0.84±0.07 for segmentation of lesions on malignant and benign breast patients, respectively. Furthermore, each of the methods, MPDL, RFM, and CSg demonstrated excellent results for breast cancer diagnosis with area under the receiver (AUC) operating characteristic (ROC) curve of 0.85, 0.91, and 0.87, respectively. Furthermore, IRIS classified patients with low risk of breast cancer recurrence from patients with medium and high risk with an AUC of 0.93 compared to OncotypeDX, a 21 gene assay for breast cancer recurrence. Conclusion: By integrating advanced computer science methods into the radiological setting, the I-GRAD framework presented in this thesis can be used to model radiological imaging data in combination with clinical and histopathological data and produce new tools for personalized diagnosis, prognosis or treatment planning by physicians

Computer-aided Detection of Breast Cancer in Digital Tomosynthesis Imaging Using Deep and Multiple Instance Learning

Breast cancer is the most common cancer among women in the world. Nevertheless, early detection of breast cancer improves the chance of successful treatment. Digital breast tomosynthesis (DBT) as a new tomographic technique was developed to minimize the limitations of conventional digital mammography screening. A DBT is a quasi-three-dimensional image that is reconstructed from a small number of two-dimensional (2D) low-dose X-ray images. The 2D X-ray images are acquired over a limited angular around the breast. Our research aims to introduce computer-aided detection (CAD) frameworks to detect early signs of breast cancer in DBTs. In this thesis, we propose three CAD frameworks for detection of breast cancer in DBTs. The first CAD framework is based on hand-crafted feature extraction. Concerning early signs of breast cancer: mass, micro-calcifications, and bilateral asymmetry between left and right breast, the system includes three separate channels to detect each sign. Next two CAD frameworks automatically learn complex patterns of 2D slices using the deep convolutional neural network and the deep cardinality-restricted Boltzmann machines. Finally, the CAD frameworks employ a multiple-instance learning approach with randomized trees algorithm to classify DBT images based on extracted information from 2D slices. The frameworks operate on 2D slices which are generated from DBT volumes. These frameworks are developed and evaluated using 5,040 2D image slices obtained from 87 DBT volumes. We demonstrate the validation and usefulness of the proposed CAD frameworks within empirical experiments for detecting breast cancer in DBTs

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis

Deep Learning in Chest Radiography: From Report Labeling to Image Classification

Chest X-ray (CXR) is the most common examination performed by a radiologist. Through CXR, radiologists must correctly and immediately diagnose a patient’s thorax to avoid the progression of life-threatening diseases. Not only are certified radiologists hard to find but also stress, fatigue, and lack of experience all contribute to the quality of an examination. As a result, providing a technique to aid radiologists in reading CXRs and a tool to help bridge the gap for communities without adequate access to radiological services would yield a huge advantage for patients and patient care. This thesis considers one essential task, CXR image classification, with Deep Learning (DL) technologies from the following three aspects: understanding the intersection of CXR interpretation and DL; extracting multiple image labels from radiology reports to facilitate the training of DL classifiers; and developing CXR classifiers using DL. First, we explain the core concepts and categorize the existing data and literature for researchers entering this field for ease of reference. Using CXRs and DL for medical image diagnosis is a relatively recent field of study because large, publicly available CXR datasets have not been around for very long. Second, we contribute to labeling large datasets with multi-label image annotations extracted from CXR reports. We describe the development of a DL-based report labeler named CXRlabeler, focusing on inductive sequential transfer learning. Lastly, we explain the design of three novel Convolutional Neural Network (CNN) classifiers, i.e., MultiViewModel, Xclassifier, and CovidXrayNet, for binary image classification, multi-label image classification, and multi-class image classification, respectively. This dissertation showcases significant progress in the field of automated CXR interpretation using DL; all source code used is publicly available. It provides methods and insights that can be applied to other medical image interpretation tasks