72 research outputs found
A Comprehensive Survey of Deep Learning: Advancements, Applications, and Challenges
Artificial intelligence's "deep learning" discipline has taken off, revolutionizing a variety of industries, from computer vision and natural language processing to healthcare and finance. Deep learning has shown extraordinary effectiveness in resolving complicated issues, and it has a wide range of potential applications, from autonomous vehicles to healthcare. The purpose of the survey to study deep learning's present condition, including recent advancements, difficulties, and constraints since the subject is currently fast growing. The basic ideas of deep learning, such as neural networks, activation functions, and optimization algorithms, are first introduced. We next explore numerous topologies, emphasizing their distinct properties and uses, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). Further concepts, applications, and difficulties of deep learning are all covered in this survey paper's thorough review. This survey aid the academics, professionals, and individuals who want to learn more about deep learning and explore its applications to challenging situations in the real world
Recuperação de informação multimodal em repositórios de imagem médica
The proliferation of digital medical imaging modalities in hospitals and other
diagnostic facilities has created huge repositories of valuable data, often
not fully explored. Moreover, the past few years show a growing trend
of data production. As such, studying new ways to index, process and
retrieve medical images becomes an important subject to be addressed by
the wider community of radiologists, scientists and engineers. Content-based
image retrieval, which encompasses various methods, can exploit the visual
information of a medical imaging archive, and is known to be beneficial to
practitioners and researchers. However, the integration of the latest systems
for medical image retrieval into clinical workflows is still rare, and their
effectiveness still show room for improvement.
This thesis proposes solutions and methods for multimodal information
retrieval, in the context of medical imaging repositories. The major
contributions are a search engine for medical imaging studies supporting
multimodal queries in an extensible archive; a framework for automated
labeling of medical images for content discovery; and an assessment and
proposal of feature learning techniques for concept detection from medical
images, exhibiting greater potential than feature extraction algorithms that
were pertinently used in similar tasks. These contributions, each in their
own dimension, seek to narrow the scientific and technical gap towards
the development and adoption of novel multimodal medical image retrieval
systems, to ultimately become part of the workflows of medical practitioners,
teachers, and researchers in healthcare.A proliferação de modalidades de imagem médica digital, em hospitais,
clínicas e outros centros de diagnóstico, levou à criação de enormes
repositórios de dados, frequentemente não explorados na sua totalidade.
Além disso, os últimos anos revelam, claramente, uma tendência para o
crescimento da produção de dados. Portanto, torna-se importante estudar
novas maneiras de indexar, processar e recuperar imagens médicas, por
parte da comunidade alargada de radiologistas, cientistas e engenheiros. A
recuperação de imagens baseada em conteúdo, que envolve uma grande
variedade de métodos, permite a exploração da informação visual num
arquivo de imagem médica, o que traz benefícios para os médicos e
investigadores. Contudo, a integração destas soluções nos fluxos de trabalho
é ainda rara e a eficácia dos mais recentes sistemas de recuperação de
imagem médica pode ser melhorada.
A presente tese propõe soluções e métodos para recuperação de informação
multimodal, no contexto de repositórios de imagem médica. As contribuições
principais são as seguintes: um motor de pesquisa para estudos de imagem
médica com suporte a pesquisas multimodais num arquivo extensível; uma
estrutura para a anotação automática de imagens; e uma avaliação e
proposta de técnicas de representation learning para deteção automática de
conceitos em imagens médicas, exibindo maior potencial do que as técnicas
de extração de features visuais outrora pertinentes em tarefas semelhantes.
Estas contribuições procuram reduzir as dificuldades técnicas e científicas
para o desenvolvimento e adoção de sistemas modernos de recuperação de
imagem médica multimodal, de modo a que estes façam finalmente parte
das ferramentas típicas dos profissionais, professores e investigadores da área
da saúde.Programa Doutoral em Informátic
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Prior to the deep learning era, shape was commonly used to describe the
objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are
predominantly diverging from computer vision, where voxel grids, meshes, point
clouds, and implicit surface models are used. This is seen from numerous
shape-related publications in premier vision conferences as well as the growing
popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915
models). For the medical domain, we present a large collection of anatomical
shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument,
called MedShapeNet, created to facilitate the translation of data-driven vision
algorithms to medical applications and to adapt SOTA vision algorithms to
medical problems. As a unique feature, we directly model the majority of shapes
on the imaging data of real patients. As of today, MedShapeNet includes 23
dataset with more than 100,000 shapes that are paired with annotations (ground
truth). Our data is freely accessible via a web interface and a Python
application programming interface (API) and can be used for discriminative,
reconstructive, and variational benchmarks as well as various applications in
virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present
use cases in the fields of classification of brain tumors, facial and skull
reconstructions, multi-class anatomy completion, education, and 3D printing. In
future, we will extend the data and improve the interfaces. The project pages
are: https://medshapenet.ikim.nrw/ and
https://github.com/Jianningli/medshapenet-feedbackComment: 16 page
MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac
MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 dataset with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, facial and skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://medshapenet.ikim.nrw/ and https://github.com/Jianningli/medshapenet-feedbac
Autoencoder-based Image Recommendation for Lung Cancer Characterization
Neste projeto, temos como objetivo desenvolver um sistema de IA que recomende um conjunto de casos relativos (passados) para orientar a tomada de decisão do médico.
Objetivo: A ambição é desenvolver um modelo de aprendizado baseado em IA para caracterização de câncer de pulmão, a fim de auxiliar na rotina clínica. Considerando a complexidade dos fenômenos biológicos que ocorrem durante o desenvolvimento do câncer, as relações entre eles e as manifestações visuais capturadas pela tomografia computadorizada (CT) têm sido exploradas nos últimos anos. No entanto, devido à falta de robustez dos métodos atuais de aprendizado profundo, essas correlações são frequentemente consideradas espúrias e se perdem quando confrontadas com dados coletados a partir de distribuições alteradas: diferentes instituições, características demográficas ou até mesmo estágios de desenvolvimento do câncer.In this project, we aim to develop an AI system that recommends a set of relative (past) cases to guide the decision-making of the clinician.
Objective: The ambition is to develop an AI-based learning model for lung cancer characterization in order to assist in clinical routine. Considering the complexity of the biological phenomenat hat occur during cancer development, relationships between these and visual manifestations captured by CT have been explored in recent years; however, given the lack of robustness of current deep learning methods, these correlations are often found spurious and get lost when facing data collected from shifted distributions: different institutions, demographics or even stages of cancer development
Anomaly detection in brain imaging
Modern healthcare systems employ a variety of medical imaging technologies, such as X-ray, MRI and CT, to improve patient outcomes, time and cost efficiency, and enable further research. Artificial intelligence and machine learning have shown promise in enhancing medical image analysis systems, leading to a proliferation of research in the field. However, many proposed approaches, such as image classification or segmentation, require large amounts of professional annotations, which are costly and time-consuming to acquire. Anomaly detection is an approach that requires less manual effort and thus can benefit from scaling to datasets of ever-increasing size.
In this thesis, we focus on anomaly localisation for pathology detection with models trained on healthy data without dense annotations. We identify two key weaknesses of current image reconstruction-based anomaly detection methods: poor image reconstruction and overdependency on pixel/voxel intensity for identification of anomalies. To address these weaknesses, we develop two novel methods: denoising autoencoder and context-tolocal feature matching, respectively.
Finally, we apply both methods to in-hospital data in collaboration with NHS Greater Glasgow and Clyde. We discuss the issues of data collection, filtering, processing, and evaluation arising in applying anomaly detection methods beyond curated datasets. We design and run a clinical evaluation contrasting our proposed methods and revealing difficulties in gauging performance of anomaly detection systems. Our findings suggest that further research is needed to fully realise the potential of anomaly detection for practical medical imaging applications. Specifically, we suggest investigating anomaly detection methods that are able to take advantage of more types of supervision (e.g. weak-labels), more context (e.g. prior scans) and make structured end-to-end predictions (e.g. bounding boxes)
Artificial Intelligence in the Creative Industries: A Review
This paper reviews the current state of the art in Artificial Intelligence
(AI) technologies and applications in the context of the creative industries. A
brief background of AI, and specifically Machine Learning (ML) algorithms, is
provided including Convolutional Neural Network (CNNs), Generative Adversarial
Networks (GANs), Recurrent Neural Networks (RNNs) and Deep Reinforcement
Learning (DRL). We categorise creative applications into five groups related to
how AI technologies are used: i) content creation, ii) information analysis,
iii) content enhancement and post production workflows, iv) information
extraction and enhancement, and v) data compression. We critically examine the
successes and limitations of this rapidly advancing technology in each of these
areas. We further differentiate between the use of AI as a creative tool and
its potential as a creator in its own right. We foresee that, in the near
future, machine learning-based AI will be adopted widely as a tool or
collaborative assistant for creativity. In contrast, we observe that the
successes of machine learning in domains with fewer constraints, where AI is
the `creator', remain modest. The potential of AI (or its developers) to win
awards for its original creations in competition with human creatives is also
limited, based on contemporary technologies. We therefore conclude that, in the
context of creative industries, maximum benefit from AI will be derived where
its focus is human centric -- where it is designed to augment, rather than
replace, human creativity
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