Discriminative cue integration for medical image annotation

Automatic annotation of medical images is an increasingly important tool for physicians in their daily activity. Hospitals nowadays produce an increasing amount of data. Manual annotation is very costly and prone to human mistakes. This paper proposes a multi-cue approach to automatic medical image annotation. We represent images using global and local features. These cues are then combined using three alternative approaches, all based on the Support Vector Machine algorithm. We tested our methods on the IRMA database, and with two of the three approaches proposed here we participated in the 2007 ImageCLEFmed benchmark evaluation, in the medical image annotation track. These algorithms ranked first and fifth respectively among all submission. Experiments using the third approach also confirm the power of cue integration for this task

Plataforma web de monitorização de dose de radiação em imagem clínica

Mestrado em Engenharia de Computadores e TelemáticaA monitorização sistemática da exposição dos cidadãos à radiação ionizante associada aos procedimentos imagiológicos é fundamental para garantir a qualidade dos serviços clínicos. Esta atividade é importante no controlo de desempenho, na optimização de protocolos e na rápida rectificação das práticas erradas. Em teoria, os episódios de radiodiagnóstico devem sempre manter a exposição à radiação tão baixa quanto razoavelmente possível (princípio ALARA), preservando a qualidade de diagnóstico. Os sistemas de monitorização de dose automáticos podem ser úteis em todas as fases de procedimentos radiológicos, ajudando os profissionais de saúde a melhorar os seus comportamentos de dosimetria. Mais ainda, a exposição aplicada nos procedimentos deve ser planeada individualmente, o que significa que a monitorização da dose também deverá ser. Além disso, o acesso integrado à história imagiológica do paciente pode ser útil para efetuar um melhor tratamento. No entanto, muitos dos atuais sistemas de informação não permitem efetuar análise de dose e a sua monitorização contínua é rara. Neste contexto, o contributo desta dissertação é o Dose Center, uma ferramenta centrada no paciente que permite monitorizar e analisar a dose de radiação. Ela tem capacidade para extrair informação proveniente de diferentes fontes e permite uma visualização integrada de toda a informação relativa aos pacientes, quais os estudos realizados, a dose efetiva e cumulativa de radiação. A ferramenta permite ainda sinalizar os casos que excedam os limites pré-definidos de radiação, uma inequívoca contribuição para a melhoria da segurança do paciente.Systematic monitoring of radiation dose exposure is a key factor to increase the quality of radiological services. This activity may lead to performance control, protocol optimization and rapid rectification of wrong practices. Moreover, dose monitoring can help the healthcare professionals to improve their dosimetric behaviors. In theory, radiodiagnostic episodes should always keep the radiation exposure as low as reasonably achievable (ALARA), while preserving the quality of diagnosis. Hence, the applied exposure in the radiology departments shall be individually planned, which means that the dose monitoring should be performed individually to ensure an appropriate dose usage. Automatic dose monitoring systems may be helpful during all the phases of radiologic procedures and the integrated access to imagiologic history may be helpful to do a better patient treatment. However, many of actual healthcare information systems do not allow dose analysis and its continuous monitoring is rare. In this context, this document proposes the Dose Center, a software platform that provides a patient-centric radiation dose analysis and a monitoring system that was designed to automatically extract and analyze dose reports captured from distinct data sources. It provides several data analytics views like, for instance, by modality or patient, including the studies effective and cumulative dose radiation. Cases exceeding the radiation thresholds are signalizing, contributing this way to improve the patient safety

The Effectiveness of Transfer Learning Systems on Medical Images

Deep neural networks have revolutionized the performances of many machine learning tasks such as medical image classification and segmentation. Current deep learning (DL) algorithms, specifically convolutional neural networks are increasingly becoming the methodological choice for most medical image analysis. However, training these deep neural networks requires high computational resources and very large amounts of labeled data which is often expensive and laborious. Meanwhile, recent studies have shown the transfer learning (TL) paradigm as an attractive choice in providing promising solutions to challenges of shortage in the availability of labeled medical images. Accordingly, TL enables us to leverage the knowledge learned from related data to solve a new problem. The objective of this dissertation is to examine the effectiveness of TL systems on medical images. First, a comprehensive systematic literature review was performed to provide an up-to-date status of TL systems on medical images. Specifically, we proposed a novel conceptual framework to organize the review. Second, a novel DL network was pretrained on natural images and utilized to evaluate the effectiveness of TL on a very large medical image dataset, specifically Chest X-rays images. Lastly, domain adaptation using an autoencoder was evaluated on the medical image dataset and the results confirmed the effectiveness of TL through fine-tuning strategies. We make several contributions to TL systems on medical image analysis: Firstly, we present a novel survey of TL on medical images and propose a new conceptual framework to organize the findings. Secondly, we propose a novel DL architecture to improve learned representations of medical images while mitigating the problem of vanishing gradients. Additionally, we identified the optimal cut-off layer (OCL) that provided the best model performance. We found that the higher layers in the proposed deep model give a better feature representation of our medical image task. Finally, we analyzed the effect of domain adaptation by fine-tuning an autoencoder on our medical images and provide theoretical contributions on the application of the transductive TL approach. The contributions herein reveal several research gaps to motivate future research and contribute to the body of literature in this active research area of TL systems on medical image analysis

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Data efficient deep learning for medical image analysis: A survey

The rapid evolution of deep learning has significantly advanced the field of medical image analysis. However, despite these achievements, the further enhancement of deep learning models for medical image analysis faces a significant challenge due to the scarcity of large, well-annotated datasets. To address this issue, recent years have witnessed a growing emphasis on the development of data-efficient deep learning methods. This paper conducts a thorough review of data-efficient deep learning methods for medical image analysis. To this end, we categorize these methods based on the level of supervision they rely on, encompassing categories such as no supervision, inexact supervision, incomplete supervision, inaccurate supervision, and only limited supervision. We further divide these categories into finer subcategories. For example, we categorize inexact supervision into multiple instance learning and learning with weak annotations. Similarly, we categorize incomplete supervision into semi-supervised learning, active learning, and domain-adaptive learning and so on. Furthermore, we systematically summarize commonly used datasets for data efficient deep learning in medical image analysis and investigate future research directions to conclude this survey.Comment: Under Revie

Biometrics

Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about