5,972 research outputs found
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 DĂ©u 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
A conceptual framework for developing dashboards for big mobility data
Dashboards are an increasingly popular form of data visualization. Large, complex, and dynamic mobility data present a number of challenges in dashboard design. The overall aim for dashboard design is to improve information communication and decision making, though big mobility data in particular require considering privacy alongside size and complexity. Taking these issues into account, a gap remains between wrangling mobility data and developing meaningful dashboard output. Therefore, there is a need for a framework that bridges this gap to support the mobility dashboard development and design process. In this paper we outline a conceptual framework for mobility data dashboards that provides guidance for the development process while considering mobility data structure, volume, complexity, varied application contexts, and privacy constraints. We illustrate the proposed framework’s components and process using example mobility dashboards with varied inputs, end-users and objectives. Overall, the framework offers a basis for developers to understand how informational displays of big mobility data are determined by end-user needs as well as the types of data selection, transformation, and display available to particular mobility datasets
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
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
Customer Segmentation: An application to dental medicine patients
Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceCustomer segmentation allows to divide a company’s customers into multiple market segments, enabling the development of customized marketing actions based on each segment’s characteristics. This work describes the application of a customer segmentation approach to the patients of a Portuguese dental company. The approach taken to select the feature subset for the final model was mostly based on the LRFM (length, recency, frequency, and monetary) model, and the monetary variable was split into multiple variables according to the treatment category where the amount was spent. K-Means and Self-organizing maps were used to cluster the company’s patients using these variables, and the results returned by both algorithms are compared. The final solution was obtained with K-Means, and 7 clusters of patients were identified. An overview of the 7 clusters is provided, and possible marketing actions are suggested based on their main characteristics. The results allowed the company to understand how its turnover was distributed across segments, and to develop an initiative to contact the patients belonging to a segment where most of them did not have an appointment in one of the company’s clinics for a long time
What works to reduce equality gaps for disabled students in higher education: Rapid Combined Evidence Review
This evidence review explores what works to reduce equality gaps for disabled students in higher education (HE) through an extensive systematic review of the academic literature, engagement with expert stakeholders, and analysis of institutional data
Modelling, Monitoring, Control and Optimization for Complex Industrial Processes
This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors
Affinity-Based Reinforcement Learning : A New Paradigm for Agent Interpretability
The steady increase in complexity of reinforcement learning (RL) algorithms is accompanied by a corresponding increase in opacity that obfuscates insights into their devised strategies. Methods in explainable artificial intelligence seek to mitigate this opacity by either creating transparent algorithms or extracting explanations post hoc. A third category exists that allows the developer to affect what agents learn: constrained RL has been used in safety-critical applications and prohibits agents from visiting certain states; preference-based RL agents have been used in robotics applications and learn state-action preferences instead of traditional reward functions. We propose a new affinity-based RL paradigm in which agents learn strategies that are partially decoupled from reward functions. Unlike entropy regularisation, we regularise the objective function with a distinct action distribution that represents a desired behaviour; we encourage the agent to act according to a prior while learning to maximise rewards. The result is an inherently interpretable agent that solves problems with an intrinsic affinity for certain actions. We demonstrate the utility of our method in a financial application: we learn continuous time-variant compositions of prototypical policies, each interpretable by its action affinities, that are globally interpretable according to customers’ financial personalities.
Our method combines advantages from both constrained RL and preferencebased RL: it retains the reward function but generalises the policy to match a defined behaviour, thus avoiding problems such as reward shaping and hacking. Unlike Boolean task composition, our method is a fuzzy superposition of different prototypical strategies to arrive at a more complex, yet interpretable, strategy.publishedVersio
Calibrating trust between humans and artificial intelligence systems
As machines become increasingly more intelligent, they become more capable of operating with greater degrees of independence from their users. However, appropriate use of these autonomous systems is dependent on appropriate trust from their users. A lack of trust towards an autonomous system will likely lead to the user doubting the capabilities of the system, potentially to the point of disuse. Conversely, too much trust in a system may lead to the user overestimating the capabilities of the system, and potentially result in errors which could have been avoided with appropriate supervision. Thus, appropriate trust is trust which is calibrated to reflect the true performance capabilities of the system. The calibration of trust towards autonomous systems is an area of research of increasing popularity, as more and more intelligent machines are introduced to modern workplaces.
This thesis contains three studies which examine trust towards autonomous technologies. In our first study, in Chapter 2, we used qualitative research methods to explore how participants characterise their trust towards different online technologies. In focus groups, participants discussed a variety of factors which they believed were important when using digital services. We had a particular interest in how they perceived social media platforms, as these services rely upon users continued sharing of their personal information. In our second study, in Chapter 3, using our initial findings we created a human-computer interaction experiment, where participants collaborated with an Autonomous Image Classifier System. In this experiment, we were able to examine the ways that participants placed trust in the classifier during different types of system performance. We also investigated whether users’ trust could be better calibrated by providing different displays of System Confidence Information, to help convey the system’s decision making. In our final study, in Chapter 4, we built directly upon the findings of Chapter 3, by creating an updated version of our human-computer interaction experiment. We provided participants with another cue of system decision making, Gradient-weighted Class Activation Mapping, and investigated whether this cue could promote greater trust towards the classifier. Additionally, we examined whether these cues can improve participants’ subjective understanding of the system’s decision making, as a way of exploring how to improve the interpretability of these systems.
This research contributes to our current understanding of calibrating users’ trust towards autonomous systems, and may be particularly useful when designing Autonomous Image Classifier Systems. While our results were inconclusive, we did find some support for users preferring the more complicated interfaces we provided. Users also reported greater understanding of the classifier’s decision making when provided with the Gradient-weighted Class Activation Mapping cue. Further research may clarify whether this cue is an appropriate method of visualising the decision-making of Autonomous Image Classifier Systems in real-world settings
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