DEVELOPING NOVEL COMPUTER-AIDED DETECTION AND DIAGNOSIS SYSTEMS OF MEDICAL IMAGES

Reading medical images to detect and diagnose diseases is often difficult and has large inter-reader variability. To address this issue, developing computer-aided detection and diagnosis (CAD) schemes or systems of medical images has attracted broad research interest in the last several decades. Despite great effort and significant progress in previous studies, only limited CAD schemes have been used in clinical practice. Thus, developing new CAD schemes is still a hot research topic in medical imaging informatics field. In this dissertation, I investigate the feasibility of developing several new innovative CAD schemes for different application purposes. First, to predict breast tumor response to neoadjuvant chemotherapy and reduce unnecessary aggressive surgery, I developed two CAD schemes of breast magnetic resonance imaging (MRI) to generate quantitative image markers based on quantitative analysis of global kinetic features. Using the image marker computed from breast MRI acquired pre-chemotherapy, CAD scheme enables to predict radiographic complete response (CR) of breast tumors to neoadjuvant chemotherapy, while using the imaging marker based on the fusion of kinetic and texture features extracted from breast MRI performed after neoadjuvant chemotherapy, CAD scheme can better predict the pathologic complete response (pCR) of the patients. Second, to more accurately predict prognosis of stroke patients, quantifying brain hemorrhage and ventricular cerebrospinal fluid depicting on brain CT images can play an important role. For this purpose, I developed a new interactive CAD tool to segment hemorrhage regions and extract radiological imaging marker to quantitatively determine the severity of aneurysmal subarachnoid hemorrhage at presentation and correlate the estimation with various homeostatic/metabolic derangements and predict clinical outcome. Third, to improve the efficiency of primary antibody screening processes in new cancer drug development, I developed a CAD scheme to automatically identify the non-negative tissue slides, which indicate reactive antibodies in digital pathology images. Last, to improve operation efficiency and reliability of storing digital pathology image data, I developed a CAD scheme using optical character recognition algorithm to automatically extract metadata from tissue slide label images and reduce manual entry for slide tracking and archiving in the tissue pathology laboratories. In summary, in these studies, we developed and tested several innovative approaches to identify quantitative imaging markers with high discriminatory power. In all CAD schemes, the graphic user interface-based visual aid tools were also developed and implemented. Study results demonstrated feasibility of applying CAD technology to several new application fields, which has potential to assist radiologists, oncologists and pathologists improving accuracy and consistency in disease diagnosis and prognosis assessment of using medical image

Prediction of near-term risk of developing breast cancer using computerized features from bilateral mammograms

abstract: Asymmetry of bilateral mammographic tissue density and patterns is a potentially strong indicator of having or developing breast abnormalities or early cancers. The purpose of this study is to design and test the global asymmetry features from bilateral mammograms to predict the near-term risk of women developing detectable high risk breast lesions or cancer in the next sequential screening mammography examination. The image dataset includes mammograms acquired from 90 women who underwent routine screening examinations, all interpreted as negative and not recalled by the radiologists during the original screening procedures. A computerized breast cancer risk analysis scheme using four image processing modules, including image preprocessing, suspicious region segmentation, image feature extraction, and classification was designed to detect and compute image feature asymmetry between the left and right breasts imaged on the mammograms. The highest computed area under curve (AUC) is 0.754 ± 0.024 when applying the new computerized aided diagnosis (CAD) scheme to our testing dataset. The positive predictive value and the negative predictive value were 0.58 and 0.80, respectively.NOTICE: this is the author's version of a work that was accepted for publication in . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in , 38, 348-357. DOI: 10.1016/j.compmedimag.2014.03.00

Applying novel machine learning technology to optimize computer-aided detection and diagnosis of medical images

The purpose of developing Computer-Aided Detection (CAD) schemes is to assist physicians (i.e., radiologists) in interpreting medical imaging findings and reducing inter-reader variability more accurately. In developing CAD schemes, Machine Learning (ML) plays an essential role because it is widely used to identify effective image features from complex datasets and optimally integrate them with the classifiers, which aims to assist the clinicians to more accurately detect early disease, classify disease types and predict disease treatment outcome. In my dissertation, in different studies, I assess the feasibility of developing several novel CAD systems in the area of medical imaging for different purposes. The first study aims to develop and evaluate a new computer-aided diagnosis (CADx) scheme based on analysis of global mammographic image features to predict the likelihood of cases being malignant. CADx scheme is applied to pre-process mammograms, generate two image maps in the frequency domain using discrete cosine transform and fast Fourier transform, compute bilateral image feature differences from left and right breasts, and apply a support vector machine (SVM) method to predict the likelihood of the case being malignant. This study demonstrates the feasibility of developing a new global image feature analysis based CADx scheme of mammograms with high performance. This new CADx approach is more efficient in development and potentially more robust in future applications by avoiding difficulty and possible errors in breast lesion segmentation. In the second study, to automatically identify a set of effective mammographic image features and build an optimal breast cancer risk stratification model, I investigate advantages of applying a machine learning approach embedded with a locally preserving projection (LPP) based feature combination and regeneration algorithm to predict short-term breast cancer risk. To this purpose, a computer-aided image processing scheme is applied to segment fibro-glandular tissue depicted on mammograms and initially compute 44 features related to the bilateral asymmetry of mammographic tissue density distribution between left and right breasts. Next, an embedded LLP algorithm optimizes the feature space and regenerates a new operational vector with 4 features using a maximal variance approach. This study demonstrates that applying the LPP algorithm effectively reduces feature dimensionality, and yields higher and potentially more robust performance in predicting short-term breast cancer risk. In the third study, to more precisely classify malignant lesions, I investigate the feasibility of applying a random projection algorithm to build an optimal feature vector from the initially CAD-generated large feature pool and improve the performance of the machine learning model. In this process, a CAD scheme is first applied to segment mass regions and initially compute 181 features. An SVM model embedded with the feature dimensionality reduction method is then built to predict the likelihood of lesions being malignant. This study demonstrates that the random project algorithm is a promising method to generate optimal feature vectors to improve the performance of machine learning models of medical images. The last study aims to develop and test a new CAD scheme of chest X-ray images to detect coronavirus (COVID-19) infected pneumonia. To this purpose, the CAD scheme first applies two image preprocessing steps to remove the majority of diaphragm regions, process the original image using a histogram equalization algorithm, and a bilateral low-pass filter. Then, the original image and two filtered images are used to form a pseudo color image. This image is fed into three input channels of a transfer learning-based convolutional neural network (CNN) model to classify chest X-ray images into 3 classes of COVID-19 infected pneumonia, other community-acquired no-COVID-19 infected pneumonia, and normal (non-pneumonia) cases. This study demonstrates that adding two image preprocessing steps and generating a pseudo color image plays an essential role in developing a deep learning CAD scheme of chest X-ray images to improve accuracy in detecting COVID-19 infected pneumonia. In summary, I developed and presented several image pre-processing algorithms, feature extraction methods, and data optimization techniques to present innovative approaches for quantitative imaging markers based on machine learning systems in all these studies. The studies' simulation and results show the discriminative performance of the proposed CAD schemes on different application fields helpful to assist radiologists on their assessments in diagnosing disease and improve their overall performance

Computer-aided detection and diagnosis of breast cancer in 2D and 3D medical imaging through multifractal analysis

This Thesis describes the research work performed in the scope of a doctoral research program and presents its conclusions and contributions. The research activities were carried on in the industry with Siemens S.A. Healthcare Sector, in integration with a research team. Siemens S.A. Healthcare Sector is one of the world biggest suppliers of products, services and complete solutions in the medical sector. The company offers a wide selection of diagnostic and therapeutic equipment and information systems. Siemens products for medical imaging and in vivo diagnostics include: ultrasound, computer tomography, mammography, digital breast tomosynthesis, magnetic resonance, equipment to angiography and coronary angiography, nuclear imaging, and many others. Siemens has a vast experience in Healthcare and at the beginning of this project it was strategically interested in solutions to improve the detection of Breast Cancer, to increase its competitiveness in the sector. The company owns several patents related with self-similarity analysis, which formed the background of this Thesis. Furthermore, Siemens intended to explore commercially the computer- aided automatic detection and diagnosis eld for portfolio integration. Therefore, with the high knowledge acquired by University of Beira Interior in this area together with this Thesis, will allow Siemens to apply the most recent scienti c progress in the detection of the breast cancer, and it is foreseeable that together we can develop a new technology with high potential. The project resulted in the submission of two invention disclosures for evaluation in Siemens A.G., two articles published in peer-reviewed journals indexed in ISI Science Citation Index, two other articles submitted in peer-reviewed journals, and several international conference papers. This work on computer-aided-diagnosis in breast led to innovative software and novel processes of research and development, for which the project received the Siemens Innovation Award in 2012. It was very rewarding to carry on such technological and innovative project in a socially sensitive area as Breast Cancer.No cancro da mama a deteção precoce e o diagnóstico correto são de extrema importância na prescrição terapêutica e caz e e ciente, que potencie o aumento da taxa de sobrevivência à doença. A teoria multifractal foi inicialmente introduzida no contexto da análise de sinal e a sua utilidade foi demonstrada na descrição de comportamentos siológicos de bio-sinais e até na deteção e predição de patologias. Nesta Tese, três métodos multifractais foram estendidos para imagens bi-dimensionais (2D) e comparados na deteção de microcalci cações em mamogramas. Um destes métodos foi também adaptado para a classi cação de massas da mama, em cortes transversais 2D obtidos por ressonância magnética (RM) de mama, em grupos de massas provavelmente benignas e com suspeição de malignidade. Um novo método de análise multifractal usando a lacunaridade tri-dimensional (3D) foi proposto para classi cação de massas da mama em imagens volumétricas 3D de RM de mama. A análise multifractal revelou diferenças na complexidade subjacente às localizações das microcalci cações em relação aos tecidos normais, permitindo uma boa exatidão da sua deteção em mamogramas. Adicionalmente, foram extraídas por análise multifractal características dos tecidos que permitiram identi car os casos tipicamente recomendados para biópsia em imagens 2D de RM de mama. A análise multifractal 3D foi e caz na classi cação de lesões mamárias benignas e malignas em imagens 3D de RM de mama. Este método foi mais exato para esta classi cação do que o método 2D ou o método padrão de análise de contraste cinético tumoral. Em conclusão, a análise multifractal fornece informação útil para deteção auxiliada por computador em mamogra a e diagnóstico auxiliado por computador em imagens 2D e 3D de RM de mama, tendo o potencial de complementar a interpretação dos radiologistas

Novel Computer-Aided Diagnosis Schemes for Radiological Image Analysis

The computer-aided diagnosis (CAD) scheme is a powerful tool in assisting clinicians (e.g., radiologists) to interpret medical images more accurately and efficiently. In developing high-performing CAD schemes, classic machine learning (ML) and deep learning (DL) algorithms play an essential role because of their advantages in capturing meaningful patterns that are important for disease (e.g., cancer) diagnosis and prognosis from complex datasets. This dissertation, organized into four studies, investigates the feasibility of developing several novel ML-based and DL-based CAD schemes for different cancer research purposes. The first study aims to develop and test a unique radiomics-based CT image marker that can be used to detect lymph node (LN) metastasis for cervical cancer patients. A total of 1,763 radiomics features were first computed from the segmented primary cervical tumor depicted on one CT image with the maximal tumor region. Next, a principal component analysis algorithm was applied on the initial feature pool to determine an optimal feature cluster. Then, based on this optimal cluster, machine learning models (e.g., support vector machine (SVM)) were trained and optimized to generate an image marker to detect LN metastasis. The SVM based imaging marker achieved an AUC (area under the ROC curve) value of 0.841 ± 0.035. This study initially verifies the feasibility of combining CT images and the radiomics technology to develop a low-cost image marker for LN metastasis detection among cervical cancer patients. In the second study, the purpose is to develop and evaluate a unique global mammographic image feature analysis scheme to identify case malignancy for breast cancer. From the entire breast area depicted on the mammograms, 59 features were initially computed to characterize the breast tissue properties in both the spatial and frequency domain. Given that each case consists of two cranio-caudal and two medio-lateral oblique view images of left and right breasts, two feature pools were built, which contain the computed features from either two positive images of one breast or all the four images of two breasts. For each feature pool, a particle swarm optimization (PSO) method was applied to determine the optimal feature cluster followed by training an SVM classifier to generate a final score for predicting likelihood of the case being malignant. The classification performances measured by AUC were 0.79±0.07 and 0.75±0.08 when applying the SVM classifiers trained using image features computed from two-view and four-view images, respectively. This study demonstrates the potential of developing a global mammographic image feature analysis-based scheme to predict case malignancy without including an arduous segmentation of breast lesions. In the third study, given that the performance of DL-based models in the medical imaging field is generally bottlenecked by a lack of sufficient labeled images, we specifically investigate the effectiveness of applying the latest transferring generative adversarial networks (GAN) technology to augment limited data for performance boost in the task of breast mass classification. This transferring GAN model was first pre-trained on a dataset of 25,000 mammogram patches (without labels). Then its generator and the discriminator were fine-tuned on a much smaller dataset containing 1024 labeled breast mass images. A supervised loss was integrated with the discriminator, such that it can be used to directly classify the benign/malignant masses. Our proposed approach improved the classification accuracy by 6.002%, when compared with the classifiers trained without traditional data augmentation. This investigation may provide a new perspective for researchers to effectively train the GAN models on a medical imaging task with only limited datasets. Like the third study, our last study also aims to alleviate DL models’ reliance on large amounts of annotations but uses a totally different approach. We propose employing a semi-supervised method, i.e., virtual adversarial training (VAT), to learn and leverage useful information underlying in unlabeled data for better classification of breast masses. Accordingly, our VAT-based models have two types of losses, namely supervised and virtual adversarial losses. The former loss acts as in supervised classification, while the latter loss works towards enhancing the model’s robustness against virtual adversarial perturbation, thus improving model generalizability. A large CNN and a small CNN were used in this investigation, and both were trained with and without the adversarial loss. When the labeled ratios were 40% and 80%, VAT-based CNNs delivered the highest classification accuracy of 0.740±0.015 and 0.760±0.015, respectively. The experimental results suggest that the VAT-based CAD scheme can effectively utilize meaningful knowledge from unlabeled data to better classify mammographic breast mass images. In summary, several innovative approaches have been investigated and evaluated in this dissertation to develop ML-based and DL-based CAD schemes for the diagnosis of cervical cancer and breast cancer. The promising results demonstrate the potential of these CAD schemes in assisting radiologists to achieve a more accurate interpretation of radiological images

RADIFUSION: A multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinement

Breast cancer is a significant public health concern and early detection is critical for triaging high risk patients. Sequential screening mammograms can provide important spatiotemporal information about changes in breast tissue over time. In this study, we propose a deep learning architecture called RADIFUSION that utilizes sequential mammograms and incorporates a linear image attention mechanism, radiomic features, a new gating mechanism to combine different mammographic views, and bilateral asymmetry-based finetuning for breast cancer risk assessment. We evaluate our model on a screening dataset called Cohort of Screen-Aged Women (CSAW) dataset. Based on results obtained on the independent testing set consisting of 1,749 women, our approach achieved superior performance compared to other state-of-the-art models with area under the receiver operating characteristic curves (AUCs) of 0.905, 0.872 and 0.866 in the three respective metrics of 1-year AUC, 2-year AUC and > 2-year AUC. Our study highlights the importance of incorporating various deep learning mechanisms, such as image attention, radiomic features, gating mechanism, and bilateral asymmetry-based fine-tuning, to improve the accuracy of breast cancer risk assessment. We also demonstrate that our model's performance was enhanced by leveraging spatiotemporal information from sequential mammograms. Our findings suggest that RADIFUSION can provide clinicians with a powerful tool for breast cancer risk assessment.Comment: v