From Fully-Supervised, Single-Task to Scarcely-Supervised, Multi-Task Deep Learning for Medical Image Analysis

Image analysis based on machine learning has gained prominence with the advent of deep learning, particularly in medical imaging. To be effective in addressing challenging image analysis tasks, however, conventional deep neural networks require large corpora of annotated training data, which are unfortunately scarce in the medical domain, thus often rendering fully-supervised learning strategies ineffective.This thesis devises for use in a variety of medical image analysis applications a series of novel deep learning methods, ranging from fully-supervised, single-task learning to scarcely-supervised, multi-task learning that makes efficient use of annotated training data. Specifically, its main contributions include (1) fully-supervised, single-task learning for the segmentation of pulmonary lobes from chest CT scans and the analysis of scoliosis from spine X-ray images; (2) supervised, single-task, domain-generalized pulmonary segmentation in chest X-ray images and retinal vasculature segmentation in fundoscopic images; (3) largely-unsupervised, multiple-task learning via deep generative modeling for the joint synthesis and classification of medical image data; and (4) partly-supervised, multiple-task learning for the combined segmentation and classification of chest and spine X-ray images

Collaborative Artificial Intelligence Algorithms for Medical Imaging Applications

In this dissertation, we propose novel machine learning algorithms for high-risk medical imaging applications. Specifically, we tackle current challenges in radiology screening process and introduce cutting-edge methods for image-based diagnosis, detection and segmentation. We incorporate expert knowledge through eye-tracking, making the whole process human-centered. This dissertation contributes to machine learning, computer vision, and medical imaging research by: 1) introducing a mathematical formulation of radiologists level of attention, and sparsifying their gaze data for a better extraction and comparison of search patterns. 2) proposing novel, local and global, image analysis algorithms. Imaging based diagnosis and pattern analysis are high-risk Artificial Intelligence applications. A standard radiology screening procedure includes detection, diagnosis and measurement (often done with segmentation) of abnormalities. We hypothesize that having a true collaboration is essential for a better control mechanism, in such applications. In this regard, we propose to form a collaboration medium between radiologists and machine learning algorithms through eye-tracking. Further, we build a generic platform consisting of novel machine learning algorithms for each of these tasks. Our collaborative algorithm utilizes eye tracking and includes an attention model and gaze-pattern analysis, based on data clustering and graph sparsification. Then, we present a semi-supervised multi-task network for local analysis of image in radiologists\u27 ROIs, extracted in the previous step. To address missing tumors and analyze regions that are completely missed by radiologists during screening, we introduce a detection framework, S4ND: Single Shot Single Scale Lung Nodule Detection. Our proposed detection algorithm is specifically designed to handle tiny abnormalities in lungs, which are easy to miss by radiologists. Finally, we introduce a novel projective adversarial framework, PAN: Projective Adversarial Network for Medical Image Segmentation, for segmenting complex 3D structures/organs, which can be beneficial in the screening process by guiding radiologists search areas through segmentation of desired structure/organ

Deep Learning for Automated Medical Image Analysis

Medical imaging is an essential tool in many areas of medical applications, used for both diagnosis and treatment. However, reading medical images and making diagnosis or treatment recommendations require specially trained medical specialists. The current practice of reading medical images is labor-intensive, time-consuming, costly, and error-prone. It would be more desirable to have a computer-aided system that can automatically make diagnosis and treatment recommendations. Recent advances in deep learning enable us to rethink the ways of clinician diagnosis based on medical images. In this thesis, we will introduce 1) mammograms for detecting breast cancers, the most frequently diagnosed solid cancer for U.S. women, 2) lung CT images for detecting lung cancers, the most frequently diagnosed malignant cancer, and 3) head and neck CT images for automated delineation of organs at risk in radiotherapy. First, we will show how to employ the adversarial concept to generate the hard examples improving mammogram mass segmentation. Second, we will demonstrate how to use the weakly labeled data for the mammogram breast cancer diagnosis by efficiently design deep learning for multi-instance learning. Third, the thesis will walk through DeepLung system which combines deep 3D ConvNets and GBM for automated lung nodule detection and classification. Fourth, we will show how to use weakly labeled data to improve existing lung nodule detection system by integrating deep learning with a probabilistic graphic model. Lastly, we will demonstrate the AnatomyNet which is thousands of times faster and more accurate than previous methods on automated anatomy segmentation.Comment: PhD Thesi

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Localisation in 3D Images Using Cross-features Correlation Learning

Object detection and segmentation have evolved drastically over the past two decades thanks to the continuous advancement in the field of deep learning. Substantial research efforts have been dedicated towards integrating object detection techniques into a wide range of real-world prob-lems. Most existing methods take advantage of the successful application and representational ability of convolutional neural networks (CNNs). Generally, these methods target mainstream applications that are typically based on 2D imaging scenarios. Additionally, driven by the strong correlation between the quality of the feature embedding and the performance in CNNs, most works focus on design characteristics of CNNs, e.g., depth and width, to enhance their modelling capacity and discriminative ability. Limited research was directed towards exploiting feature-level dependencies, which can be feasibly used to enhance the performance of CNNs. More-over, directly adopting such approaches into more complex imaging domains that target data of higher dimensions (e.g., 3D multi-modal and volumetric images) is not straightforwardly appli-cable due to the different nature and complexity of the problem. In this thesis, we explore the possibility of incorporating feature-level correspondence and correlations into object detection and segmentation contexts that target the localisation of 3D objects from 3D multi-modal and volumetric image data. Accordingly, we first explore the detection problem of 3D solar active regions in multi-spectral solar imagery where different imaging bands correspond to different 2D layers (altitudes) in the 3D solar atmosphere.We propose a joint analysis approach in which information from different imaging bands is first individually analysed using band-specific network branches to extract inter-band features that are then dynamically cross-integrated and jointly analysed to investigate spatial correspon-dence and co-dependencies between the different bands. The aggregated embeddings are further analysed using band-specific detection network branches to predict separate sets of results (one for each band). Throughout our study, we evaluate different types of feature fusion, using convo-lutional embeddings of different semantic levels, as well as the impact of using different numbers of image bands inputs to perform the joint analysis. We test the proposed approach over different multi-modal datasets (multi-modal solar images and brain MRI) and applications. The proposed joint analysis based framework consistently improves the CNN’s performance when detecting target regions in contrast to single band based baseline methods.We then generalise our cross-band joint analysis detection scheme into the 3D segmentation problem using multi-modal images. We adopt the joint analysis principles into a segmentation framework where cross-band information is dynamically analysed and cross-integrated at vari-ous semantic levels. The proposed segmentation network also takes advantage of band-specific skip connections to maximise the inter-band information and assist the network in capturing fine details using embeddings of different spatial scales. Furthermore, a recursive training strat-egy, based on weak labels (e.g., bounding boxes), is proposed to overcome the difficulty of producing dense labels to train the segmentation network. We evaluate the proposed segmen-tation approach using different feature fusion approaches, over different datasets (multi-modal solar images, brain MRI, and cloud satellite imagery), and using different levels of supervisions. Promising results were achieved and demonstrate an improved performance in contrast to single band based analysis and state-of-the-art segmentation methods.Additionally, we investigate the possibility of explicitly modelling objective driven feature-level correlations, in a localised manner, within 3D medical imaging scenarios (3D CT pul-monary imaging) to enhance the effectiveness of the feature extraction process in CNNs and subsequently the detection performance. Particularly, we present a framework to perform the 3D detection of pulmonary nodules as an ensemble of two stages, candidate proposal and a false positive reduction. We propose a 3D channel attention block in which cross-channel informa-tion is incorporated to infer channel-wise feature importance with respect to the target objective. Unlike common attention approaches that rely on heavy dimensionality reduction and computa-tionally expensive multi-layer perceptron networks, the proposed approach utilises fully convo-lutional networks to allow directly exploiting rich 3D descriptors and performing the attention in an efficient manner. We also propose a fully convolutional 3D spatial attention approach that elevates cross-sectional information to infer spatial attention. We demonstrate the effectiveness of the proposed attention approaches against a number of popular channel and spatial attention mechanisms. Furthermore, for the False positive reduction stage, in addition to attention, we adopt a joint analysis based approach that takes into account the variable nodule morphology by aggregating spatial information from different contextual levels. We also propose a Zoom-in convolutional path that incorporates semantic information of different spatial scales to assist the network in capturing fine details. The proposed detection approach demonstrates considerable gains in performance in contrast to state-of-the-art lung nodule detection methods.We further explore the possibility of incorporating long-range dependencies between arbi-trary positions in the input features using Transformer networks to infer self-attention, in the context of 3D pulmonary nodule detection, in contrast to localised (convolutional based) atten-tion . We present a hybrid 3D detection approach that takes advantage of both, the Transformers ability in modelling global context and correlations and the spatial representational characteris-tics of convolutional neural networks, providing complementary information and subsequently improving the discriminative ability of the detection model. We propose two hybrid Transformer CNN variants where we investigate the impact of exploiting a deeper Transformer design –in which more Transformer layers and trainable parameters are incorporated– is used along with high-level convolutional feature inputs of a single spatial resolution, in contrast to a shallower Transformer design –of less Transformer layers and trainable parameters– while exploiting con-volutional embeddings of different semantic levels and relatively higher resolution.Extensive quantitative and qualitative analyses are presented for the proposed methods in this thesis and demonstrate the feasibility of exploiting feature-level relations, either implicitly or explicitly, in different detection and segmentation problems

Advanced machine learning methods for oncological image analysis

Cancer is a major public health problem, accounting for an estimated 10 million deaths worldwide in 2020 alone. Rapid advances in the field of image acquisition and hardware development over the past three decades have resulted in the development of modern medical imaging modalities that can capture high-resolution anatomical, physiological, functional, and metabolic quantitative information from cancerous organs. Therefore, the applications of medical imaging have become increasingly crucial in the clinical routines of oncology, providing screening, diagnosis, treatment monitoring, and non/minimally- invasive evaluation of disease prognosis. The essential need for medical images, however, has resulted in the acquisition of a tremendous number of imaging scans. Considering the growing role of medical imaging data on one side and the challenges of manually examining such an abundance of data on the other side, the development of computerized tools to automatically or semi-automatically examine the image data has attracted considerable interest. Hence, a variety of machine learning tools have been developed for oncological image analysis, aiming to assist clinicians with repetitive tasks in their workflow. This thesis aims to contribute to the field of oncological image analysis by proposing new ways of quantifying tumor characteristics from medical image data. Specifically, this thesis consists of six studies, the first two of which focus on introducing novel methods for tumor segmentation. The last four studies aim to develop quantitative imaging biomarkers for cancer diagnosis and prognosis. The main objective of Study I is to develop a deep learning pipeline capable of capturing the appearance of lung pathologies, including lung tumors, and integrating this pipeline into the segmentation networks to leverage the segmentation accuracy. The proposed pipeline was tested on several comprehensive datasets, and the numerical quantifications show the superiority of the proposed prior-aware DL framework compared to the state of the art. Study II aims to address a crucial challenge faced by supervised segmentation models: dependency on the large-scale labeled dataset. In this study, an unsupervised segmentation approach is proposed based on the concept of image inpainting to segment lung and head- neck tumors in images from single and multiple modalities. The proposed autoinpainting pipeline shows great potential in synthesizing high-quality tumor-free images and outperforms a family of well-established unsupervised models in terms of segmentation accuracy. Studies III and IV aim to automatically discriminate the benign from the malignant pulmonary nodules by analyzing the low-dose computed tomography (LDCT) scans. In Study III, a dual-pathway deep classification framework is proposed to simultaneously take into account the local intra-nodule heterogeneities and the global contextual information. Study IV seeks to compare the discriminative power of a series of carefully selected conventional radiomics methods, end-to-end Deep Learning (DL) models, and deep features-based radiomics analysis on the same dataset. The numerical analyses show the potential of fusing the learned deep features into radiomic features for boosting the classification power. Study V focuses on the early assessment of lung tumor response to the applied treatments by proposing a novel feature set that can be interpreted physiologically. This feature set was employed to quantify the changes in the tumor characteristics from longitudinal PET-CT scans in order to predict the overall survival status of the patients two years after the last session of treatments. The discriminative power of the introduced imaging biomarkers was compared against the conventional radiomics, and the quantitative evaluations verified the superiority of the proposed feature set. Whereas Study V focuses on a binary survival prediction task, Study VI addresses the prediction of survival rate in patients diagnosed with lung and head-neck cancer by investigating the potential of spherical convolutional neural networks and comparing their performance against other types of features, including radiomics. While comparable results were achieved in intra- dataset analyses, the proposed spherical-based features show more predictive power in inter-dataset analyses. In summary, the six studies incorporate different imaging modalities and a wide range of image processing and machine-learning techniques in the methods developed for the quantitative assessment of tumor characteristics and contribute to the essential procedures of cancer diagnosis and prognosis

Deep Learning of Unified Region, Edge, and Contour Models for Automated Image Segmentation

Image segmentation is a fundamental and challenging problem in computer vision with applications spanning multiple areas, such as medical imaging, remote sensing, and autonomous vehicles. Recently, convolutional neural networks (CNNs) have gained traction in the design of automated segmentation pipelines. Although CNN-based models are adept at learning abstract features from raw image data, their performance is dependent on the availability and size of suitable training datasets. Additionally, these models are often unable to capture the details of object boundaries and generalize poorly to unseen classes. In this thesis, we devise novel methodologies that address these issues and establish robust representation learning frameworks for fully-automatic semantic segmentation in medical imaging and mainstream computer vision. In particular, our contributions include (1) state-of-the-art 2D and 3D image segmentation networks for computer vision and medical image analysis, (2) an end-to-end trainable image segmentation framework that unifies CNNs and active contour models with learnable parameters for fast and robust object delineation, (3) a novel approach for disentangling edge and texture processing in segmentation networks, and (4) a novel few-shot learning model in both supervised settings and semi-supervised settings where synergies between latent and image spaces are leveraged to learn to segment images given limited training data.Comment: PhD dissertation, UCLA, 202