32 research outputs found
A Review of the Role of Causality in Developing Trustworthy AI Systems
State-of-the-art AI models largely lack an understanding of the cause-effect
relationship that governs human understanding of the real world. Consequently,
these models do not generalize to unseen data, often produce unfair results,
and are difficult to interpret. This has led to efforts to improve the
trustworthiness aspects of AI models. Recently, causal modeling and inference
methods have emerged as powerful tools. This review aims to provide the reader
with an overview of causal methods that have been developed to improve the
trustworthiness of AI models. We hope that our contribution will motivate
future research on causality-based solutions for trustworthy AI.Comment: 55 pages, 8 figures. Under revie
Medical image retrieval for augmenting diagnostic radiology
Even though the use of medical imaging to diagnose patients is ubiquitous in clinical settings, their interpretations are still challenging for radiologists. Many factors make this interpretation task difficult, one of which is that medical images sometimes present subtle clues yet are crucial for diagnosis. Even worse, on the other hand, similar clues could indicate multiple diseases, making it challenging to figure out the definitive diagnoses. To help radiologists quickly and accurately interpret medical images, there is a need for a tool that can augment their diagnostic procedures and increase efficiency in their daily workflow. A general-purpose medical image retrieval system can be such a
tool as it allows them to search and retrieve similar cases that are already diagnosed to make comparative analyses that would complement their diagnostic decisions. In this thesis, we contribute to developing such a system by proposing approaches to be integrated as modules of a single system, enabling it to handle various information needs of radiologists and thus augment their diagnostic processes during the interpretation of medical images.
We have mainly studied the following retrieval approaches to handle radiologistsâdifferent information needs; i) Retrieval Based on Contents, ii) Retrieval Based on Contents, Patientsâ Demographics, and Disease Predictions, and iii) Retrieval Based on Contents and Radiologistsâ Text Descriptions. For the first study, we aimed to find an effective feature representation method to distinguish medical images considering their semantics and modalities. To do that, we have experimented different representation techniques based on handcrafted methods (mainly texture features) and deep learning (deep features). Based on the experimental results, we propose an effective feature representation approach and deep learning architectures for learning and extracting medical image contents. For the second study, we present a multi-faceted method that complements image contents with patientsâ demographics and deep learning-based disease predictions, making it able to identify similar cases accurately considering the clinical context the radiologists seek.
For the last study, we propose a guided search method that integrates an image with a radiologistâs text description to guide the retrieval process. This method guarantees that the retrieved images are suitable for the comparative analysis to confirm or rule
out initial diagnoses (the differential diagnosis procedure). Furthermore, our method is based on a deep metric learning technique and is better than traditional content-based approaches that rely on only image features and, thus, sometimes retrieve insignificant random images
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends
Computer vision (CV) is a big and important field
in artificial intelligence covering a wide range of applications.
Image analysis is a major task in CV aiming to extract, analyse
and understand the visual content of images. However, imagerelated
tasks are very challenging due to many factors, e.g., high
variations across images, high dimensionality, domain expertise
requirement, and image distortions. Evolutionary computation
(EC) approaches have been widely used for image analysis with
significant achievement. However, there is no comprehensive
survey of existing EC approaches to image analysis. To fill
this gap, this paper provides a comprehensive survey covering
all essential EC approaches to important image analysis tasks
including edge detection, image segmentation, image feature
analysis, image classification, object detection, and others. This
survey aims to provide a better understanding of evolutionary
computer vision (ECV) by discussing the contributions of different
approaches and exploring how and why EC is used for
CV and image analysis. The applications, challenges, issues, and
trends associated to this research field are also discussed and
summarised to provide further guidelines and opportunities for
future research
Dynamic And Quantitative Radiomics Analysis In Interventional Radiology
Interventional Radiology (IR) is a subspecialty of radiology that performs invasive procedures driven by diagnostic imaging for predictive and therapeutic purpose. The development of artificial intelligence (AI) has revolutionized the industry of IR. Researchers have created sophisticated models backed by machine learning algorithms and optimization methodologies for image registration, cellular structure detection and computer-aided disease diagnosis and prognosis predictions. However, due to the incapacity of the human eye to detect tiny structural characteristics and inter-radiologist heterogeneity, conventional experience-based IR visual evaluations may have drawbacks.
Radiomics, a technique that utilizes machine learning, offers a practical and quantifiable solution to this issue. This technology has been used to evaluate the heterogeneity of malignancies that are difficult to detect by the human eye by creating an automated pipeline for the extraction and analysis of high throughput computational imaging characteristics from radiological medical pictures. However, it is a demanding task to directly put radiomics into applications in IR because of the heterogeneity and complexity of medical imaging data. Furthermore, recent radiomics studies are based on static images, while many clinical applications (such as detecting the occurrence and development of tumors and assessing patient response to chemotherapy and immunotherapy) is a dynamic process. Merely incorporating static features cannot comprehensively reflect the metabolic characteristics and dynamic processes of tumors or soft tissues.
To address these issues, we proposed a robust feature selection framework to manage the high-dimensional small-size data. Apart from that, we explore and propose a descriptor in the view of computer vision and physiology by integrating static radiomics features with time-varying information in tumor dynamics. The major contributions to this study include:
Firstly, we construct a result-driven feature selection framework, which could efficiently reduce the dimension of the original feature set. The framework integrates different feature selection techniques to ensure the distinctiveness, uniqueness, and generalization ability of the output feature set. In the task of classification hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) in primary liver cancer, only three radiomics features (chosen from more than 1, 800 features of the proposed framework) can obtain an AUC of 0.83 in the independent dataset. Besides, we also analyze featuresâ pattern and contributions to the results, enhancing clinical interpretability of radiomics biomarkers.
Secondly, we explore and build a pulmonary perfusion descriptor based on 18F-FDG whole-body dynamic PET images. Our major novelties include: 1) propose a physiology-and-computer-vision-interpretable descriptor construction framework by the decomposition of spatiotemporal information into three dimensions: shades of grey levels, textures, and dynamics. 2) The spatio-temporal comparison of pulmonary descriptor intra and inter patients is feasible, making it possible to be an auxiliary diagnostic tool in pulmonary function assessment. 3) Compared with traditional PET metabolic biomarker analysis, the proposed descriptor incorporates imageâs temporal information, which enables a better understanding of the time-various mechanisms and detection of visual perfusion abnormalities among different patients. 4) The proposed descriptor eliminates the impact of vascular branching structure and gravity effect by utilizing time warping algorithms. Our experimental results showed that our proposed framework and descriptor are promising tools to medical imaging analysis
Towards Secure and Intelligent Diagnosis: Deep Learning and Blockchain Technology for Computer-Aided Diagnosis Systems
Cancer is the second leading cause of death across the world after cardiovascular disease. The survival rate of patients with cancerous tissue can significantly decrease due to late-stage diagnosis. Nowadays, advancements of whole slide imaging scanners have resulted in a dramatic increase of patient data in the domain of digital pathology. Large-scale histopathology images need to be analyzed promptly for early cancer detection which is critical for improving patient's survival rate and treatment planning. Advances of medical image processing and deep learning methods have facilitated the extraction and analysis of high-level features from histopathological data that could assist in life-critical diagnosis and reduce the considerable healthcare cost associated with cancer. In clinical trials, due to the complexity and large variance of collected image data, developing computer-aided diagnosis systems to support quantitative medical image analysis is an area of active research. The first goal of this research is to automate the classification and segmentation process of cancerous regions in histopathology images of different cancer tissues by developing models using deep learning-based architectures. In this research, a framework with different modules is proposed, including (1) data pre-processing, (2) data augmentation, (3) feature extraction, and (4) deep learning architectures. Four validation studies were designed to conduct this research. (1) differentiating benign and malignant lesions in breast cancer (2) differentiating between immature leukemic blasts and normal cells in leukemia cancer (3) differentiating benign and malignant regions in lung cancer, and (4) differentiating benign and malignant regions in colorectal cancer.
Training machine learning models, disease diagnosis, and treatment often requires collecting patients' medical data. Privacy and trusted authenticity concerns make data owners reluctant to share their personal and medical data. Motivated by the advantages of Blockchain technology in healthcare data sharing frameworks, the focus of the second part of this research is to integrate Blockchain technology in computer-aided diagnosis systems to address the problems of managing access control, authentication, provenance, and confidentiality of sensitive medical data. To do so, a hierarchical identity and attribute-based access control mechanism using smart contract and Ethereum Blockchain is proposed to securely process healthcare data without revealing sensitive information to an unauthorized party leveraging the trustworthiness of transactions in a collaborative healthcare environment. The proposed access control mechanism provides a solution to the challenges associated with centralized access control systems and ensures data transparency and traceability for secure data sharing, and data ownership
Supervised deep semantics-preserving hashing for real-time pulmonary nodule image retrieval
Hashing-based medical image retrieval has drawn extensive attention recently, which aims at providing effective aided diagnosis for medical personnel. In the paper, a novel deep hashing framework is proposed in the medical image retrieval, where the processes of deep feature extraction, binary code learning, and deep hash function learning are jointly carried out in supervised fashion. Particularly, the discrete constrained objective function in the hash code learning is optimized iteratively, where the binary code can be directly solved with no need for relaxation. In the meantime, the semantic similarity is maintained by fully exploring supervision information during the discrete optimization, where the neighborhood structure of training data is preserved by applying a graph regularization term. Additionally, to gain the fine-grained ranking of the returned medical images sharing the same Hamming distance, a novel image re-ranking scheme is proposed to refine the similarity measurement by jointly considering Euclidean distance between the real-valued feature descriptors and their category information between those images. Extensive experiments on the pulmonary nodule image dataset demonstrate that the proposed method can achieve better retrieval performance over the state of the arts
Enhancing Breast Cancer Prediction Using Unlabeled Data
Selles vĂ€itekirjas esitatakse sildistamata andmeid kasutav sĂŒvaĂ”ppe lĂ€henemine rinna infiltratiivse duktaalse kartsinoomi koeregioonide automaatseks klassifitseerimiseks rinnavĂ€hi patoloogilistes digipreparaatides. SĂŒvaĂ”ppe meetodite tööpĂ”himĂ”te on sarnane inimajule, mis töötab samuti mitmetel tĂ”lgendustasanditel. Need meetodid on osutunud tulemuslikeks ka vĂ€ga keerukate probleemide nagu pildiliigituse ja esemetuvastuse lahendamisel, ĂŒletades seejuures varasemate lahendusviiside efektiivsust. SĂŒvaĂ”ppeks on aga vaja suurt hulka sildistatud andmeid, mida vĂ”ib olla keeruline saada, eriti veel meditsiinis, kuna nii haiglad kui ka patsiendid ei pruugi olla nĂ”us sedavĂ”rd delikaatset teavet loovutama. Lisaks sellele on masinĂ”ppesĂŒsteemide saavutatavate aina paremate tulemuste hinnaks nende sĂŒsteemide sisemise keerukuse kasv. Selle sisemise keerukuse tĂ”ttu muutub raskemaks ka nende sĂŒsteemide töö mĂ”istmine, mistĂ”ttu kasutajad ei kipu neid usaldama. Meditsiinilisi diagnoose ei saa jĂ€rgida pimesi, kuna see vĂ”ib endaga kaasa tuua patsiendi tervise kahjustamise. Mudeli mĂ”istetavuse tagamine on seega oluline viis sĂŒsteemi usaldatavuse tĂ”stmiseks, eriti just masinĂ”ppel pĂ”hinevate mudelite laialdasel rakendamisel sellistel kriitilise tĂ€htsusega aladel nagu seda on meditsiin. Infiltratiivne duktaalne kartsinoom on ĂŒks levinumaid ja ka agressiivsemaid rinnavĂ€hi vorme, moodustades peaaegu 80% kĂ”igist juhtumitest. Selle diagnoosimine on patoloogidele vĂ€ga keerukas ja ajakulukas ĂŒlesanne, kuna nĂ”uab vĂ”imalike pahaloomuliste kasvajate avastamiseks paljude healoomuliste piirkondade uurimist. Samas on infiltratiivse duktaalse kartsinoomi digipatoloogias tĂ€pne piiritlemine vĂ€hi agressiivsuse hindamise aspektist ĂŒlimalt oluline. KĂ€esolevas uurimuses kasutatakse konvolutsioonilist nĂ€rvivĂ”rku arendamaks vĂ€lja infiltratiivse duktaalse kartsinoomi diagnoosimisel rakendatav pooleldi juhitud Ă”ppe skeem. VĂ€lja pakutud raamistik suurendab esmalt vĂ€ikest sildistatud andmete hulka generatiivse vĂ”istlusliku vĂ”rgu loodud sĂŒnteetiliste meditsiiniliste kujutistega. SeejĂ€rel kasutatakse juba eelnevalt treenitud vĂ”rku, et selle suurendatud andmekogumi peal lĂ€bi viia kujutuvastus, misjĂ€rel sildistamata andmed sildistatakse andmesildistusalgoritmiga. Töötluse tulemusena saadud sildistatud andmeid eelmainitud konvolutsioonilisse nĂ€rvivĂ”rku sisestades saavutatakse rahuldav tulemus: ROC kĂ”vera alla jÀÀv pindala ja F1 skoor on vastavalt 0.86 ja 0.77. Lisaks sellele vĂ”imaldavad vĂ€lja pakutud mĂ”istetavuse tĂ”stmise tehnikad nĂ€ha ka meditsiinilistele prognooside otsuse tegemise protsessi seletust, mis omakorda teeb sĂŒsteemi usaldamise kasutajatele lihtsamaks. KĂ€esolev uurimus nĂ€itab, et konvolutsioonilise nĂ€rvivĂ”rgu tehtud otsuseid aitab paremini mĂ”ista see, kui kasutajatele visualiseeritakse konkreetse juhtumi puhul infiltratiivse duktaalse kartsinoomi positiivse vĂ”i negatiivse otsuse langetamisel sĂŒsteemi jaoks kĂ”ige olulisemaks osutunud piirkondi.The following thesis presents a deep learning (DL) approach for automatic classification of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BC) using unlabeled data. DL methods are similar to the way the human brain works across different interpretation levels. These techniques have shown to outperform traditional approaches of the most complex problems such as image classification and object detection. However, DL requires a broad set of labeled data that is difficult to obtain, especially in the medical field as neither the hospitals nor the patients are willing to reveal such sensitive information. Moreover, machine learning (ML) systems are achieving better performance at the cost of becoming increasingly complex. Because of that, they become less interpretable that causes distrust from the users. Model interpretability is a way to enhance trust in a system. It is a very desirable property, especially crucial with the pervasive adoption of ML-based models in the critical domains like the medical field. With medical diagnostics, the predictions cannot be blindly followed as it may result in harm to the patient. IDC is one of the most common and aggressive subtypes of all breast cancers accounting nearly 80% of them. Assessment of the disease is a very time-consuming and challenging task for pathologists, as it involves scanning large swatches of benign regions to identify an area of malignancy. Meanwhile, accurate delineation of IDC in WSI is crucial for the estimation of grading cancer aggressiveness. In the following study, a semi-supervised learning (SSL) scheme is developed using the deep convolutional neural network (CNN) for IDC diagnosis. The proposed framework first augments a small set of labeled data with synthetic medical images, generated by the generative adversarial network (GAN) that is followed by feature extraction using already pre-trained network on the larger dataset and a data labeling algorithm that labels a much broader set of unlabeled data. After feeding the newly labeled set into the proposed CNN model, acceptable performance is achieved: the AUC and the F-measure accounting for 0.86, 0.77, respectively. Moreover, proposed interpretability techniques produce explanations for medical predictions and build trust in the presented CNN. The following study demonstrates that it is possible to enable a better understanding of the CNN decisions by visualizing areas that are the most important for a particular prediction and by finding elements that are the reasons for IDC, Non-IDC decisions made by the network