460 research outputs found
DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation
In this work, we propose an AI-based method that intends to improve the
conventional retinal disease treatment procedure and help ophthalmologists
increase diagnosis efficiency and accuracy. The proposed method is composed of
a deep neural networks-based (DNN-based) module, including a retinal disease
identifier and clinical description generator, and a DNN visual explanation
module. To train and validate the effectiveness of our DNN-based module, we
propose a large-scale retinal disease image dataset. Also, as ground truth, we
provide a retinal image dataset manually labeled by ophthalmologists to
qualitatively show, the proposed AI-based method is effective. With our
experimental results, we show that the proposed method is quantitatively and
qualitatively effective. Our method is capable of creating meaningful retinal
image descriptions and visual explanations that are clinically relevant.Comment: Accepted to IEEE WACV 202
Machine Learning Methods for Image Analysis in Medical Applications, from Alzheimer\u27s Disease, Brain Tumors, to Assisted Living
Healthcare has progressed greatly nowadays owing to technological advances, where machine learning plays an important role in processing and analyzing a large amount of medical data. This thesis investigates four healthcare-related issues (Alzheimer\u27s disease detection, glioma classification, human fall detection, and obstacle avoidance in prosthetic vision), where the underlying methodologies are associated with machine learning and computer vision. For Alzheimer’s disease (AD) diagnosis, apart from symptoms of patients, Magnetic Resonance Images (MRIs) also play an important role. Inspired by the success of deep learning, a new multi-stream multi-scale Convolutional Neural Network (CNN) architecture is proposed for AD detection from MRIs, where AD features are characterized in both the tissue level and the scale level for improved feature learning. Good classification performance is obtained for AD/NC (normal control) classification with test accuracy 94.74%. In glioma subtype classification, biopsies are usually needed for determining different molecular-based glioma subtypes. We investigate non-invasive glioma subtype prediction from MRIs by using deep learning. A 2D multi-stream CNN architecture is used to learn the features of gliomas from multi-modal MRIs, where the training dataset is enlarged with synthetic brain MRIs generated by pairwise Generative Adversarial Networks (GANs). Test accuracy 88.82% has been achieved for IDH mutation (a molecular-based subtype) prediction. A new deep semi-supervised learning method is also proposed to tackle the problem of missing molecular-related labels in training datasets for improving the performance of glioma classification. In other two applications, we also address video-based human fall detection by using co-saliency-enhanced Recurrent Convolutional Networks (RCNs), as well as obstacle avoidance in prosthetic vision by characterizing obstacle-related video features using a Spiking Neural Network (SNN). These investigations can benefit future research, where artificial intelligence/deep learning may open a new way for real medical applications
Towards Unsupervised Domain Adaptation for Diabetic Retinopathy Detection in the Tromsø Eye Study
Diabetic retinopathy (DR) is an eye disease which affects a third of the diabetic population. It is a preventable disease, but requires early detection for efficient treatment. While there has been increasing interest in applying
deep learning techniques for DR detection in order to aid practitioners make more accurate diagnosis, these efforts are mainly focused on datasets that have been collected or created with ML in mind. In this thesis, however,
we take a look at two particular datasets that have been collected at the University Hospital of North-Norway - UNN.
These datasets have inherent problems that motivate the methodological choices in this work such as a variable number of input images and domain shift.
We therefore contribute a multi-stream model for DR classification. The multi-stream model can model dependency across different images, can take in a variable of input of any size, is general in its detection such that the
image processing is equal no matter which stream the image enters, and is compatible with the domain adaptation method ADDA, but we argue the model is compatible with many other methods. As a remedy for these problems, we propose a multi-stream deep learning architecture that is uniquely tailored to these datasets and illustrate how
domain adaptation might be utilized within the framework to learn efficiently in the presence of domain shift.
Our experiments demonstrates the models properties empirically, and shows it can deal with each of the presented problems. The model this paper contributes is a first step towards DR detection from these local datasets
and, in the bigger picture, similar datasets worldwide
Query-controllable Video Summarization
When video collections become huge, how to explore both within and across
videos efficiently is challenging. Video summarization is one of the ways to
tackle this issue. Traditional summarization approaches limit the effectiveness
of video exploration because they only generate one fixed video summary for a
given input video independent of the information need of the user. In this
work, we introduce a method which takes a text-based query as input and
generates a video summary corresponding to it. We do so by modeling video
summarization as a supervised learning problem and propose an end-to-end deep
learning based method for query-controllable video summarization to generate a
query-dependent video summary. Our proposed method consists of a video summary
controller, video summary generator, and video summary output module. To foster
the research of query-controllable video summarization and conduct our
experiments, we introduce a dataset that contains frame-based relevance score
labels. Based on our experimental result, it shows that the text-based query
helps control the video summary. It also shows the text-based query improves
our model performance. Our code and dataset:
https://github.com/Jhhuangkay/Query-controllable-Video-Summarization.Comment: This paper is accepted by ACM International Conference on Multimedia
Retrieval (ICMR), 202
Exploring variability in medical imaging
Although recent successes of deep learning and novel machine learning techniques improved the perfor-
mance of classification and (anomaly) detection in computer vision problems, the application of these
methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this
is the amount of variability that is encountered and encapsulated in human anatomy and subsequently
reflected in medical images. This fundamental factor impacts most stages in modern medical imaging
processing pipelines.
Variability of human anatomy makes it virtually impossible to build large datasets for each disease
with labels and annotation for fully supervised machine learning. An efficient way to cope with this is
to try and learn only from normal samples. Such data is much easier to collect. A case study of such
an automatic anomaly detection system based on normative learning is presented in this work. We
present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative
models, which are trained only utilising normal/healthy subjects.
However, despite the significant improvement in automatic abnormality detection systems, clinical
routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis
and localise abnormalities. Integrating human expert knowledge into the medical imaging processing
pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per-
spective of building an automated medical imaging system, it is still an open issue, to what extent
this kind of variability and the resulting uncertainty are introduced during the training of a model
and how it affects the final performance of the task. Consequently, it is very important to explore the
effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as
on the model’s performance in a specific machine learning task. A thorough investigation of this issue
is presented in this work by leveraging automated estimates for machine learning model uncertainty,
inter-observer variability and segmentation task performance in lung CT scan images.
Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging
was attempted. This state-of-the-art survey includes both conventional pattern recognition methods
and deep learning based methods. It is one of the first literature surveys attempted in the specific
research area.Open Acces
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in
medical imaging. However, these approaches primarily focus on supervised
learning, assuming that the training and testing data are drawn from the same
distribution. Unfortunately, this assumption may not always hold true in
practice. To address these issues, unsupervised domain adaptation (UDA)
techniques have been developed to transfer knowledge from a labeled domain to a
related but unlabeled domain. In recent years, significant advancements have
been made in UDA, resulting in a wide range of methodologies, including feature
alignment, image translation, self-supervision, and disentangled representation
methods, among others. In this paper, we provide a comprehensive literature
review of recent deep UDA approaches in medical imaging from a technical
perspective. Specifically, we categorize current UDA research in medical
imaging into six groups and further divide them into finer subcategories based
on the different tasks they perform. We also discuss the respective datasets
used in the studies to assess the divergence between the different domains.
Finally, we discuss emerging areas and provide insights and discussions on
future research directions to conclude this survey.Comment: Under Revie
Comprehensive Survey: Biometric User Authentication Application, Evaluation, and Discussion
This paper conducts an extensive review of biometric user authentication
literature, addressing three primary research questions: (1) commonly used
biometric traits and their suitability for specific applications, (2)
performance factors such as security, convenience, and robustness, and
potential countermeasures against cyberattacks, and (3) factors affecting
biometric system accuracy and po-tential improvements. Our analysis delves into
physiological and behavioral traits, exploring their pros and cons. We discuss
factors influencing biometric system effectiveness and highlight areas for
enhancement. Our study differs from previous surveys by extensively examining
biometric traits, exploring various application domains, and analyzing measures
to mitigate cyberattacks. This paper aims to inform researchers and
practitioners about the biometric authentication landscape and guide future
advancements
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