1,593 research outputs found
Optimizing U-Net Architecture with Feed-Forward Neural Networks for Precise Cobb Angle Prediction in Scoliosis Diagnosis
In the burgeoning field of Artificial Intelligence (AI) and its notable subsets, such as Deep Learning (DL), there is evidence of its transformative impact in assisting clinicians, particularly in diagnosing scoliosis. AI is unrivaled for its speed and precision in analyzing medical images, including X-rays and computed tomography (CT) scans. However, the path does not lack obstacles. Biases, unanticipated outcomes, and false positive and negative predictions present significant challenges. Our research employed three complex experimental sets, each focusing on adapting the U-Net architecture. Through a nuanced combination of feed-forward neural network (FFNN) configurations and hyperparameters, we endeavored to determine the most effective nonlinear regression model configuration for predicting the Cobb angle. This was done with the dual purpose of reducing AI training time without sacrificing predictive accuracy. Utilizing the capabilities of the PyTorch framework, we meticulously crafted and refined the deep learning models for each of the three experiments, focusing on an FFFN dropout rate of p=0.45. The Root Mean Square Error (RMSE), the number of epochs, and the number of nodes spanning three hidden layers in each FFFN were utilized as crucial performance metrics while a base learning rate of 0.001 was maintained. Notably, during the optimization phase, one of the experiments incorporated a learning rate scheduler to protect against potential pitfalls such as local minima and saddle points. A judiciously incorporated Early Stopping technique, triggered between the patience range of 5-10 epochs, ensured model stability as the Mean Squared Error (MSE) plateau loss approached approximately 1. Consequently, the model converged between 50 and 82 epochs. We hypothesize that our proposed architecture holds promise for future refinements, conditioned on assiduous experimentation with an array of medical deep learning paradigms
Scaling Up Medical Visualization : Multi-Modal, Multi-Patient, and Multi-Audience Approaches for Medical Data Exploration, Analysis and Communication
Medisinsk visualisering er en av de mest applikasjonsrettede omrÄdene av visualiseringsforsking. Tett samarbeid med medisinske eksperter er nÞdvendig for Ä tolke medisinsk bildedata og lage betydningsfulle visualiseringsteknikker og visualiseringsapplikasjoner. Kreft er en av de vanligste dÞdsÄrsakene, og med Þkende gjennomsnittsalder i i-land Þker ogsÄ antallet diagnoser av gynekologisk kreft. Moderne avbildningsteknikker er et viktig verktÞy for Ä vurdere svulster og produsere et Þkende antall bildedata som radiologer mÄ tolke. I tillegg til antallet bildemodaliteter, Þker ogsÄ antallet pasienter, noe som fÞrer til at visualiseringslÞsninger mÄ bli skalert opp for Ä adressere den Þkende kompleksiteten av multimodal- og multipasientdata. Dessuten er ikke medisinsk visualisering kun tiltenkt medisinsk personale, men har ogsÄ som mÄl Ä informere pasienter, pÄrÞrende, og offentligheten om risikoen relatert til visse sykdommer, og mulige behandlinger. Derfor har vi identifisert behovet for Ä skalere opp medisinske visualiseringslÞsninger for Ä kunne hÄndtere multipublikumdata.
Denne avhandlingen adresserer skaleringen av disse dimensjonene i forskjellige bidrag vi har kommet med. FĂžrst presenterer vi teknikkene vĂ„re for Ă„ skalere visualiseringer i flere modaliteter. Vi introduserer en visualiseringsteknikk som tar i bruk smĂ„ multipler for Ă„ vise data fra flere modaliteter innenfor et bildesnitt. Dette lar radiologer utforske dataen effektivt uten Ă„ mĂ„tte bruke flere sidestilte vinduer. I det neste steget utviklet vi en analyseplatform ved Ă„ ta i bruk «radiomic tumor profiling» pĂ„ forskjellige bildemodaliteter for Ă„ analysere kohortdata og finne nye biomarkĂžrer fra bilder. BiomarkĂžrer fra bilder er indikatorer basert pĂ„ bildedata som kan forutsi variabler relatert til kliniske utfall. «Radiomic tumor profiling» er en teknikk som genererer mulige biomarkĂžrer fra bilder basert pĂ„ fĂžrste- og andregrads statistiske mĂ„linger. Applikasjonen lar medisinske eksperter analysere multiparametrisk bildedata for Ă„ finne mulige korrelasjoner mellom kliniske parameter og data fra «radiomic tumor profiling». Denne tilnĂŠrmingen skalerer i to dimensjoner, multimodal og multipasient. I en senere versjon la vi til funksjonalitet for Ă„ skalere multipublikumdimensjonen ved Ă„ gjĂžre applikasjonen vĂ„r anvendelig for livmorhalskreft- og prostatakreftdata, i tillegg til livmorkreftdataen som applikasjonen var designet for. I et senere bidrag fokuserer vi pĂ„ svulstdata pĂ„ en annen skala og muliggjĂžr analysen av svulstdeler ved Ă„ bruke multimodal bildedata i en tilnĂŠrming basert pĂ„ hierarkisk gruppering. Applikasjonen vĂ„r finner mulige interessante regioner som kan informere fremtidige behandlingsavgjĂžrelser. I et annet bidrag, en digital sonderingsinteraksjon, fokuserer vi pĂ„ multipasientdata. Bildedata fra flere pasienter kan sammenlignes for Ă„ finne interessante mĂžnster i svulstene som kan vĂŠre knyttet til hvor aggressive svulstene er. Til slutt skalerer vi multipublikumdimensjonen med en likhetsvisualisering som er anvendelig for forskning pĂ„ livmorkreft, pĂ„ bilder av nevrologisk kreft, og maskinlĂŠringsforskning pĂ„ automatisk segmentering av svulstdata. Som en kontrast til de allerede fremhevete bidragene, fokuserer vĂ„rt siste bidrag, ScrollyVis, hovedsakelig pĂ„ multipublikumkommunikasjon. Vi muliggjĂžr skapelsen av dynamiske og vitenskapelige âscrollytellingâ-opplevelser for spesifikke eller generelle publikum. Slike historien kan bli brukt i spesifikke brukstilfeller som kommunikasjon mellom lege og pasient, eller for Ă„ kommunisere vitenskapelige resultater via historier til et generelt publikum i en digital museumsutstilling.
VÄre foreslÄtte applikasjoner og interaksjonsteknikker har blitt demonstrert i brukstilfeller og evaluert med domeneeksperter og fokusgrupper. Dette har fÞrt til at noen av vÄre bidrag allerede er i bruk pÄ andre forskingsinstitusjoner. Vi Þnsker Ä evaluere innvirkningen deres pÄ andre vitenskapelige felt og offentligheten i fremtidige arbeid.Medical visualization is one of the most application-oriented areas of visualization research. Close collaboration with medical experts is essential for interpreting medical imaging data and creating meaningful visualization techniques and visualization applications. Cancer is one of the most common causes of death, and with increasing average age in developed countries, gynecological malignancy case numbers are rising. Modern imaging techniques are an essential tool in assessing tumors and produce an increasing number of imaging data radiologists must interpret. Besides the number of imaging modalities, the number of patients is also rising, leading to visualization solutions that must be scaled up to address the rising complexity of multi-modal and multi-patient data. Furthermore, medical visualization is not only targeted toward medical professionals but also has the goal of informing patients, relatives, and the public about the risks of certain diseases and potential treatments. Therefore, we identify the need to scale medical visualization solutions to cope with multi-audience data.
This thesis addresses the scaling of these dimensions in different contributions we made. First, we present our techniques to scale medical visualizations in multiple modalities. We introduced a visualization technique using small multiples to display the data of multiple modalities within one imaging slice. This allows radiologists to explore the data efficiently without having several juxtaposed windows. In the next step, we developed an analysis platform using radiomic tumor profiling on multiple imaging modalities to analyze cohort data and to find new imaging biomarkers. Imaging biomarkers are indicators based on imaging data that predict clinical outcome related variables. Radiomic tumor profiling is a technique that generates potential imaging biomarkers based on first and second-order statistical measurements. The application allows medical experts to analyze the multi-parametric imaging data to find potential correlations between clinical parameters and the radiomic tumor profiling data. This approach scales up in two dimensions, multi-modal and multi-patient. In a later version, we added features to scale the multi-audience dimension by making our application applicable to cervical and prostate cancer data and the endometrial cancer data the application was designed for. In a subsequent contribution, we focus on tumor data on another scale and enable the analysis of tumor sub-parts by using the multi-modal imaging data in a hierarchical clustering approach. Our application finds potentially interesting regions that could inform future treatment decisions. In another contribution, the digital probing interaction, we focus on multi-patient data. The imaging data of multiple patients can be compared to find interesting tumor patterns potentially linked to the aggressiveness of the tumors. Lastly, we scale the multi-audience dimension with our similarity visualization applicable to endometrial cancer research, neurological cancer imaging research, and machine learning research on the automatic segmentation of tumor data. In contrast to the previously highlighted contributions, our last contribution, ScrollyVis, focuses primarily on multi-audience communication. We enable the creation of dynamic scientific scrollytelling experiences for a specific or general audience. Such stories can be used for specific use cases such as patient-doctor communication or communicating scientific results via stories targeting the general audience in a digital museum exhibition.
Our proposed applications and interaction techniques have been demonstrated in application use cases and evaluated with domain experts and focus groups. As a result, we brought some of our contributions to usage in practice at other research institutes. We want to evaluate their impact on other scientific fields and the general public in future work.Doktorgradsavhandlin
Transdisciplinary AI Observatory -- Retrospective Analyses and Future-Oriented Contradistinctions
In the last years, AI safety gained international recognition in the light of
heterogeneous safety-critical and ethical issues that risk overshadowing the
broad beneficial impacts of AI. In this context, the implementation of AI
observatory endeavors represents one key research direction. This paper
motivates the need for an inherently transdisciplinary AI observatory approach
integrating diverse retrospective and counterfactual views. We delineate aims
and limitations while providing hands-on-advice utilizing concrete practical
examples. Distinguishing between unintentionally and intentionally triggered AI
risks with diverse socio-psycho-technological impacts, we exemplify a
retrospective descriptive analysis followed by a retrospective counterfactual
risk analysis. Building on these AI observatory tools, we present near-term
transdisciplinary guidelines for AI safety. As further contribution, we discuss
differentiated and tailored long-term directions through the lens of two
disparate modern AI safety paradigms. For simplicity, we refer to these two
different paradigms with the terms artificial stupidity (AS) and eternal
creativity (EC) respectively. While both AS and EC acknowledge the need for a
hybrid cognitive-affective approach to AI safety and overlap with regard to
many short-term considerations, they differ fundamentally in the nature of
multiple envisaged long-term solution patterns. By compiling relevant
underlying contradistinctions, we aim to provide future-oriented incentives for
constructive dialectics in practical and theoretical AI safety research
Computer Architecture in Industrial, Biomechanical and Biomedical Engineering
This book aims to provide state-of-the-art information on computer architecture and simulation in industry, engineering, and clinical scenarios. Accepted submissions are high in scientific value and provide a significant contribution to computer architecture. Each submission expands upon novel and innovative research where the methods, analysis, and conclusions are robust and of the highest standard. This book is a valuable resource for researchers, students, non-governmental organizations, and key decision-makers involved in earthquake disaster management systems at the national, regional, and local levels
Proceedings of the First Workshop on Computing News Storylines (CNewsStory 2015)
This volume contains the proceedings of the 1st Workshop on Computing News Storylines (CNewsStory
2015) held in conjunction with the 53rd Annual Meeting of the Association for Computational
Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP
2015) at the China National Convention Center in Beijing, on July 31st 2015.
Narratives are at the heart of information sharing. Ever since people began to share their experiences,
they have connected them to form narratives. The study od storytelling and the field of literary theory
called narratology have developed complex frameworks and models related to various aspects of
narrative such as plots structures, narrative embeddings, charactersâ perspectives, reader response, point
of view, narrative voice, narrative goals, and many others. These notions from narratology have been
applied mainly in Artificial Intelligence and to model formal semantic approaches to narratives (e.g.
Plot Units developed by Lehnert (1981)). In recent years, computational narratology has qualified as an
autonomous field of study and research. Narrative has been the focus of a number of workshops and
conferences (AAAI Symposia, Interactive Storytelling Conference (ICIDS), Computational Models of
Narrative). Furthermore, reference annotation schemes for narratives have been proposed (NarrativeML
by Mani (2013)).
The workshop aimed at bringing together researchers from different communities working on
representing and extracting narrative structures in news, a text genre which is highly used in NLP
but which has received little attention with respect to narrative structure, representation and analysis.
Currently, advances in NLP technology have made it feasible to look beyond scenario-driven, atomic
extraction of events from single documents and work towards extracting story structures from multiple
documents, while these documents are published over time as news streams. Policy makers, NGOs,
information specialists (such as journalists and librarians) and others are increasingly in need of tools
that support them in finding salient stories in large amounts of information to more effectively implement
policies, monitor actions of âbig playersâ in the society and check facts. Their tasks often revolve around
reconstructing cases either with respect to specific entities (e.g. person or organizations) or events (e.g.
hurricane Katrina). Storylines represent explanatory schemas that enable us to make better selections
of relevant information but also projections to the future. They form a valuable potential for exploiting
news data in an innovative way.JRC.G.2-Global security and crisis managemen
Directional adposition use in English, Swedish and Finnish
Directional adpositions such as to the left of describe where a Figure is in relation to a Ground. English and Swedish directional adpositions refer to the location of a Figure in relation to a Ground, whether both are static or in motion. In contrast, the Finnish directional adpositions edellÀ (in front of) and jÀljessÀ (behind) solely describe the location of a moving Figure in relation to a moving Ground (Nikanne, 2003).
When using directional adpositions, a frame of reference must be assumed for interpreting the meaning of directional adpositions. For example, the meaning of to the left of in English can be based on a relative (speaker or listener based) reference frame or an intrinsic (object based) reference frame (Levinson, 1996). When a Figure and a Ground are both in motion, it is possible for a Figure to be described as being behind or in front of the Ground, even if neither have intrinsic features. As shown by Walker (in preparation), there are good reasons to assume that in the latter case a motion based reference frame is involved. This means that if Finnish speakers would use edellÀ (in front of) and jÀljessÀ (behind) more frequently in situations where both the Figure and Ground are in motion, a difference in reference frame use between Finnish on one hand and English and Swedish on the other could be expected.
We asked native English, Swedish and Finnish speakersâ to select adpositions from a language specific list to describe the location of a Figure relative to a Ground when both were shown to be moving on a computer screen. We were interested in any differences between Finnish, English and Swedish speakers.
All languages showed a predominant use of directional spatial adpositions referring to the lexical concepts TO THE LEFT OF, TO THE RIGHT OF, ABOVE and BELOW. There were no differences between the languages in directional adpositions use or reference frame use, including reference frame use based on motion.
We conclude that despite differences in the grammars of the languages involved, and potential differences in reference frame system use, the three languages investigated encode Figure location in relation to Ground location in a similar way when both are in motion.
Levinson, S. C. (1996). Frames of reference and Molyneuxâs question: Crosslingiuistic evidence. In P. Bloom, M.A. Peterson, L. Nadel & M.F. Garrett (Eds.) Language and Space (pp.109-170). Massachusetts: MIT Press.
Nikanne, U. (2003). How Finnish postpositions see the axis system. In E. van der Zee & J. Slack (Eds.), Representing direction in language and space. Oxford, UK: Oxford University Press.
Walker, C. (in preparation). Motion encoding in language, the use of spatial locatives in a motion context. Unpublished doctoral dissertation, University of Lincoln, Lincoln. United Kingdo
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