Development and analysis of image and video coding and indexing method for medical examinations with applications in telemedicine

Orientadores: Wu Feng Chung, Huei Diana LeeTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Ciências MédicasResumo: O surgimento da videoendoscopia digestiva alta e baixa e da telemedicina constituem importantes avanços tecnológicos para o diagnóstico e o treinamento em procedimentos de exames do trato gastrointestinal por métodos ópticos. Nesse cenário, a transmissão e o armazenamento de imagens e vídeos digitais demandam técnicas de compressão adequadas às características da aplicação. Além disso, com a formação de grandes bases de dados, a recuperação por conteúdo das imagens de modo efetivo é também necessária. Assim sendo, este trabalho propõe um método original de codificação, indexação e recuperação de imagens e vídeos. O método usa a decomposição em valores singulares como técnica algébrica de fatoração que permite a ordenação dos fatores constituintes por nível de importância na constituição da imagem. O método apresenta, também, uma análise estatística prévia de cores obtidas de amostras representativas, formando um mapa de probabilidade baseado na frequência de cores, e que resulta na representação compacta das mesmas. Por meio de experimentos realizados com 2.700 quadros de endoscopia digestiva alta, o método de codificação alcançou taxa de compressão média de 82,07%, com desvio padrão de 6,37%, e qualidade objetiva de 38,85 dB com desvio padrão de 1,06 dB. Em comparação com o padrão de codificação H.263, houve diferença estatisticamente significativa em termos da taxa de compressão e da qualidade objetiva (p ? 0,05). Para a recuperação, foram indexados 7.212 quadros e utilizadas 97 imagens de exemplo para a busca. Como resultado, a precisão média alcançada foi de 94,85% na configuração que obteve o melhor desempenho geral, tendo sido igual ou superior a outros métodos reportados na literatura. Por meio dos experimentos realizados conclui-se que o método original proposto foi eficaz em codificar e indexar automaticamente vídeos de exames médicos para aplicações de telemedicina.Abstract: The emergence of upper and lower gastrointestinal (GI) video endoscopy and telemedicine are essential technological advancements for the diagnosis and training related to examination procedures of the digestive tract using optical methods. In this context, transmission and storage of digital images and videos require compression techniques that are suitable for the application characteristics. Moreover, as the media databases grow massive, effective search and retrieval by image content become necessary. Thus, this work proposes an original method for coding, indexing and retrieving images and videos. The proposed method uses the singular value decomposition as the algebraic technique for matrix factorization that allows sorting the components by their level of relevance in the image composition. The process also presents a statistical analysis of colors, obtained from typical image samples, to form a probability map based on the color frequency, that further leads to a compact representation. Through the experiments executed on 2,700 frames of upper GI endoscopy, the coding method achieved an average compression ratio of 82.07% with a standard deviation of 6.37%, and objective quality of 38.85 dB with a standard deviation of 1.06 dB. In comparison with the H.263 coding standard, it was observed a statistically significant difference in terms of compression ratio and objective quality (p ? 0,05). As for the retrieval, 7212 frames were indexed and 97 sample images were used as search queries. As a result, an average precision of 94,85% was achieved for the configuration with best overall performance, outperforming other methods reported in the literature. Based on the results of the experiments it is possible to conclude that the proposed original method was effectively able to encode and automatically index medical examination videos for telemedicine applicationsDoutoradoFisiopatologia CirúrgicaDoutor em Ciência

Fine-tuning pre-trained neural networks for medical image classification in small clinical datasets

Funding We would like to acknowledge eurekaSD: Enhancing University Research and Education in Areas Useful for Sustainable Development - grants EK14AC0037 and EK15AC0264. We thank Araucária Foundation for the Support of the Scientific and Technological Development of Paraná through a Research and Technological Productivity Scholarship for H. D. Lee (grant 028/2019). We also thank the Brazilian National Council for Scientific and Technological Development (CNPq) through the grant number 142050/2019-9 for A. R. S. Parmezan. The Portuguese team was partially supported by Fundação para a Ciência e a Tecnologia (FCT). R. Fonseca-Pinto was financed by the projects UIDB/50008/2020, UIDP/50008/2020, UIDB/05704/2020 and UIDP/05704/2020 and C. V. Nogueira was financed by the projects UIDB/00013/2020 and UIDP/00013/2020. The funding agencies did not have any further involvement in this paper.Convolutional neural networks have been effective in several applications, arising as a promising supporting tool in a relevant Dermatology problem: skin cancer diagnosis. However, generalizing well can be difficult when little training data is available. The fine-tuning transfer learning strategy has been employed to differentiate properly malignant from non-malignant lesions in dermoscopic images. Fine-tuning a pre-trained network allows one to classify data in the target domain, occasionally with few images, using knowledge acquired in another domain. This work proposes eight fine-tuning settings based on convolutional networks previously trained on ImageNet that can be employed mainly in limited data samples to reduce overfitting risk. They differ on the architecture, the learning rate and the number of unfrozen layer blocks. We evaluated the settings in two public datasets with 104 and 200 dermoscopic images. By finding competitive configurations in small datasets, this paper illustrates that deep learning can be effective if one has only a few dozen malignant and non-malignant lesion images to study and differentiate in Dermatology. The proposal is also flexible and potentially useful for other domains. In fact, it performed satisfactorily in an assessment conducted in a larger dataset with 746 computerized tomographic images associated with the coronavirus disease.info:eu-repo/semantics/publishedVersio

A computational system based on ontologies to automate the mapping process of medical reports into structured databases

We have developed, in collaboration with medical and computer experts, the ontology-based Medical Report Mapping Process to support the transformation of unstructured reports into a structured representation. Nevertheless, the techniques employed in this two-phase process must be performed individually and manually by computer instructions, which hinder their use by users not familiar with such language. Thereby, this work proposes a tool to automate and optimize this process by integrating its techniques in a computational system, which was built using a software engineering prototyping approach. This system was experimentally evaluated by applying it to a set of 100 textual reports. The first phase decreased the total number of phrases (853) and words (2520) by 82.25% (48) and 92.70% (184), respectively. In the second phase, 100% of the relevant pieces of information (previously established) present in the reports were transcribed. Also, the second phase was applied, using the same configuration as the first study, in another set with 250 textual reports, resulting in a mapping rate of 99.74%. The unprocessed and unmapped words, regarding both experimental evaluations, were recorded for later inclusion into the ontology. By using this system, efficient and scalable investigations can be performed from medical reports, contributing to generate new knowledge. Also, the system facilitates the definition of these structures due to the feasibility to analyze different sentences in unique phrase sets.1153756COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESSem informaçã