6,603 research outputs found
Using Online Video Observations and Real Time, Peer Reflective Analysis of Culturally Responsive Teaching Pedagogy in a University Teacher Preparatory Program for Preservice Teachers
This research aimed to understand the impacts of using online video observations and real-time peer reflection to teach and address culturally responsive teaching in a Pacific Northwest university’s teacher preparatory program. Six active university students enrolled in a university’s new teacher preparatory program (i.e., preservice, new teacher candidates) actively participated in all areas of this study (i.e., nonrandom sampling) and provided both quantitative and qualitative data. Study participants completed self-evaluative pre- and post-surveys in a research group session. Surveys were built using the ready 4 rigor framework (Hammond & Jackson, 2015) and the four areas of culturally responsive teaching as a foundation for a psychometric response scale (i.e., Likert scale 1–5) and peer reflection prompts. In group settings, study participants watched videos of their peers and themselves engaging in classroom instruction. After video observations, they participated in real-time, peer reflective analysis of teaching performance. Using a quantitative and qualitative approach to analyze the pre- and post-survey responses and reflective discussions, data revealed participants gained a deeper understanding of their ability to deliver culturally responsive teaching pedagogy. Overall, these data points suggested a change in participant awareness of culturally responsive teaching performance levels before and after engaging in video observations and real-time, peer reflective analysis involving culturally responsive teaching pedagogy
Segmentation of Pathology Images: A Deep Learning Strategy with Annotated Data
Cancer has significantly threatened human life and health for many years. In the clinic, histopathology image segmentation is the golden stand for evaluating the prediction of patient prognosis and treatment outcome. Generally, manually labelling tumour regions in hundreds of high-resolution histopathological images is time-consuming and expensive for pathologists. Recently, the advancements in hardware and computer vision have allowed deep-learning-based methods to become mainstream to segment tumours automatically, significantly reducing the workload of pathologists. However, most current methods rely on large-scale labelled histopathological images. Therefore, this research studies label-effective tumour segmentation methods using deep-learning paradigms to relieve the annotation limitations. Chapter 3 proposes an ensemble framework for fully-supervised tumour segmentation. Usually, the performance of an individual-trained network is limited by significant morphological variances in histopathological images. We propose a fully-supervised learning ensemble fusion model that uses both shallow and deep U-Nets, trained with images of different resolutions and subsets of images, for robust predictions of tumour regions. Noise elimination is achieved with Convolutional Conditional Random Fields. Two open datasets are used to evaluate the proposed method: the ACDC@LungHP challenge at ISBI2019 and the DigestPath challenge at MICCAI2019. With a dice coefficient of 79.7 %, the proposed method takes third place in ACDC@LungHP. In DigestPath 2019, the proposed method achieves a dice coefficient 77.3 %. Well-annotated images are an indispensable part of training fully-supervised segmentation strategies. However, large-scale histopathology images are hardly annotated finely in clinical practice. It is common for labels to be of poor quality or for only a few images to be manually marked by experts. Consequently, fully-supervised methods cannot perform well in these cases. Chapter 4 proposes a self-supervised contrast learning for tumour segmentation. A self-supervised cancer segmentation framework is proposed to reduce label dependency. An innovative contrastive learning scheme is developed to represent tumour features based on unlabelled images. Unlike a normal U-Net, the backbone is a patch-based segmentation network. Additionally, data augmentation and contrastive losses are applied to improve the discriminability of tumour features. A convolutional Conditional Random Field is used to smooth and eliminate noise. Three labelled, and fourteen unlabelled images are collected from a private skin cancer dataset called BSS. Experimental results show that the proposed method achieves better tumour segmentation performance than other popular self-supervised methods. However, by evaluated on the same public dataset as chapter 3, the proposed self-supervised method is hard to handle fine-grained segmentation around tumour boundaries compared to the supervised method we proposed. Chapter 5 proposes a sketch-based weakly-supervised tumour segmentation method. To segment tumour regions precisely with coarse annotations, a sketch-supervised method is proposed, containing a dual CNN-Transformer network and a global normalised class activation map. CNN-Transformer networks simultaneously model global and local tumour features. With the global normalised class activation map, a gradient-based tumour representation can be obtained from the dual network predictions. We invited experts to mark fine and coarse annotations in the private BSS and the public PAIP2019 datasets to facilitate reproducible performance comparisons. Using the BSS dataset, the proposed method achieves 76.686 % IOU and 86.6 % Dice scores, outperforming state-of-the-art methods. Additionally, the proposed method achieves a Dice gain of 8.372 % compared with U-Net on the PAIP2019 dataset. The thesis presents three approaches to segmenting cancers from histology images: fully-supervised, unsupervised, and weakly supervised methods. This research effectively segments tumour regions based on histopathological annotations and well-designed modules. Our studies comprehensively demonstrate label-effective automatic histopathological image segmentation. Experimental results prove that our works achieve state-of-the-art segmentation performances on private and public datasets. In the future, we plan to integrate more tumour feature representation technologies with other medical modalities and apply them to clinical research
Evaluation of different segmentation-based approaches for skin disorders from dermoscopic images
Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, Roser, Mata Miquel, Christian, Munuera, JosepSkin disorders are the most common type of cancer in the world and the incident has been lately increasing over the past decades. Even with the most complex and advanced technologies, current image acquisition systems do not permit a reliable identification of the skin lesion by visual examination due to the challenging structure of the malignancy. This promotes the need for the implementation of automatic skin lesion segmentation methods in order to assist in physicians’ diagnostic when determining the lesion's region and to serve as a preliminary step for the classification of the skin lesion. Accurate and precise segmentation is crucial for a rigorous screening and monitoring of the disease's progression.
For the purpose of the commented concern, the present project aims to accomplish a state-of-the-art review about the most predominant conventional segmentation models for skin lesion segmentation, alongside with a market analysis examination. With the rise of automatic segmentation tools, a wide number of algorithms are currently being used, but many are the drawbacks when employing them for dermatological disorders due to the high-level presence of artefacts in the image acquired.
In light of the above, three segmentation techniques have been selected for the completion of the work: level set method, an algorithm combining GrabCut and k-means methods and an intensity automatic algorithm developed by Hospital Sant Joan de Déu de Barcelona research group. In addition, a validation of their performance is conducted for a further implementation of them in clinical training. The proposals, together with the got outcomes, have been accomplished by means of a publicly available skin lesion image database
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
Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment. FMER is a subset of image processing and it
is a multidisciplinary topic to analysis. So, it requires familiarity with
other topics of Artifactual Intelligence (AI) such as machine learning, digital
image processing, psychology and more. So, it is a great opportunity to write a
book which covers all of these topics for beginner to professional readers in
the field of AI and even without having background of AI. Our goal is to
provide a standalone introduction in the field of MFER analysis in the form of
theorical descriptions for readers with no background in image processing with
reproducible Matlab practical examples. Also, we describe any basic definitions
for FMER analysis and MATLAB library which is used in the text, that helps
final reader to apply the experiments in the real-world applications. We
believe that this book is suitable for students, researchers, and professionals
alike, who need to develop practical skills, along with a basic understanding
of the field. We expect that, after reading this book, the reader feels
comfortable with different key stages such as color and depth image processing,
color and depth image representation, classification, machine learning, facial
micro-expressions recognition, feature extraction and dimensionality reduction.
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment.Comment: This is the second edition of the boo
PIKS: A Technique to Identify Actionable Trends for Policy-Makers Through Open Healthcare Data
With calls for increasing transparency, governments are releasing greater
amounts of data in multiple domains including finance, education and
healthcare. The efficient exploratory analysis of healthcare data constitutes a
significant challenge. Key concerns in public health include the quick
identification and analysis of trends, and the detection of outliers. This
allows policies to be rapidly adapted to changing circumstances. We present an
efficient outlier detection technique, termed PIKS (Pruned iterative-k means
searchlight), which combines an iterative k-means algorithm with a pruned
searchlight based scan. We apply this technique to identify outliers in two
publicly available healthcare datasets from the New York Statewide Planning and
Research Cooperative System, and California's Office of Statewide Health
Planning and Development. We provide a comparison of our technique with three
other existing outlier detection techniques, consisting of auto-encoders,
isolation forests and feature bagging. We identified outliers in conditions
including suicide rates, immunity disorders, social admissions,
cardiomyopathies, and pregnancy in the third trimester. We demonstrate that the
PIKS technique produces results consistent with other techniques such as the
auto-encoder. However, the auto-encoder needs to be trained, which requires
several parameters to be tuned. In comparison, the PIKS technique has far fewer
parameters to tune. This makes it advantageous for fast, "out-of-the-box" data
exploration. The PIKS technique is scalable and can readily ingest new
datasets. Hence, it can provide valuable, up-to-date insights to citizens,
patients and policy-makers. We have made our code open source, and with the
availability of open data, other researchers can easily reproduce and extend
our work. This will help promote a deeper understanding of healthcare policies
and public health issues
A Visual Modeling Method for Spatiotemporal and Multidimensional Features in Epidemiological Analysis: Applied COVID-19 Aggregated Datasets
The visual modeling method enables flexible interactions with rich graphical
depictions of data and supports the exploration of the complexities of
epidemiological analysis. However, most epidemiology visualizations do not
support the combined analysis of objective factors that might influence the
transmission situation, resulting in a lack of quantitative and qualitative
evidence. To address this issue, we have developed a portrait-based visual
modeling method called +msRNAer. This method considers the spatiotemporal
features of virus transmission patterns and the multidimensional features of
objective risk factors in communities, enabling portrait-based exploration and
comparison in epidemiological analysis. We applied +msRNAer to aggregate
COVID-19-related datasets in New South Wales, Australia, which combined
COVID-19 case number trends, geo-information, intervention events, and
expert-supervised risk factors extracted from LGA-based censuses. We perfected
the +msRNAer workflow with collaborative views and evaluated its feasibility,
effectiveness, and usefulness through one user study and three subject-driven
case studies. Positive feedback from experts indicates that +msRNAer provides a
general understanding of analyzing comprehension that not only compares
relationships between cases in time-varying and risk factors through portraits
but also supports navigation in fundamental geographical, timeline, and other
factor comparisons. By adopting interactions, experts discovered functional and
practical implications for potential patterns of long-standing community
factors against the vulnerability faced by the pandemic. Experts confirmed that
+msRNAer is expected to deliver visual modeling benefits with spatiotemporal
and multidimensional features in other epidemiological analysis scenarios
DIN Spec 91345 RAMI 4.0 compliant data pipelining: An approach to support data understanding and data acquisition in smart manufacturing environments
Today, data scientists in the manufacturing domain are confronted with a set of challenges associated to data acquisition as well as data processing including the extraction of valuable in-formation to support both, the work of the manufacturing equipment as well as the manufacturing processes behind it.
One essential aspect related to data acquisition is the pipelining, including various commu-nication standards, protocols and technologies to save and transfer heterogenous data. These circumstances make it hard to understand, find, access and extract data from the sources depend-ing on use cases and applications.
In order to support this data pipelining process, this thesis proposes the use of the semantic model. The selected semantic model should be able to describe smart manufacturing assets them-selves as well as to access their data along their life-cycle.
As a matter of fact, there are many research contributions in smart manufacturing, which already came out with reference architectures or standards for semantic-based meta data descrip-tion or asset classification. This research builds upon these outcomes and introduces a novel se-mantic model-based data pipelining approach using as a basis the Reference Architecture Model for Industry 4.0 (RAMI 4.0).Hoje em dia, os cientistas de dados no domÃnio da manufatura são confrontados com várias normas, protocolos e tecnologias de comunicação para gravar, processar e transferir vários tipos de dados. Estas circunstâncias tornam difÃcil compreender, encontrar, aceder e extrair dados necessários para aplicações dependentes de casos de utilização, desde os equipamentos aos respectivos processos de manufatura.
Um aspecto essencial poderia ser um processo de canalisação de dados incluindo vários normas de comunicação, protocolos e tecnologias para gravar e transferir dados. Uma solução para suporte deste processo, proposto por esta tese, é a aplicação de um modelo semântico que descreva os próprios recursos de manufactura inteligente e o acesso aos seus dados ao longo do seu ciclo de vida.
Muitas das contribuições de investigação em manufatura inteligente já produziram arquitecturas de referência como a RAMI 4.0 ou normas para a descrição semântica de meta dados ou classificação de recursos. Esta investigação baseia-se nestas fontes externas e introduz um novo modelo semântico baseado no Modelo de Arquitectura de Referência para Indústria 4.0 (RAMI 4.0), em conformidade com a abordagem de canalisação de dados no domÃnio da produção inteligente como caso exemplar de utilização para permitir uma fácil exploração, compreensão, descoberta, selecção e extracção de dados
- …