| 1. |
- Candefjord, Stefan, 1981, et al.
(författare)
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Microwave technology for detecting traumatic intracranial bleedings: tests on phantom of subdural hematoma and numerical simulations
- 2017
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Ingår i: Medical and Biological Engineering and Computing. - : Springer Science and Business Media LLC. - 1741-0444 .- 0140-0118. ; 55:8, s. 1177-1188
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Tidskriftsartikel (refereegranskat)abstract
- Traumatic brain injury is the leading cause of death and severe disability for young people and a major public health problem for elderly. Many patients with intracranial bleeding are treated too late, because they initially show no symptoms of severe injury and are not transported to a trauma center. There is a need for a method to detect intracranial bleedings in the prehospital setting. In this study, we investigate whether broadband microwave technology (MWT) in conjunction with a diagnostic algorithm can detect subdural hematoma (SDH). A human cranium phantom and numerical simulations of SDH are used. Four phantoms with SDH 0, 40, 70 and 110 mL are measured with a MWT instrument. The simulated dataset consists of 1500 observations. Classification accuracy is assessed using fivefold cross-validation, and a validation dataset never used for training. The total accuracy is 100 and 82–96 % for phantom measurements and simulated data, respectively. Sensitivity and specificity for bleeding detection were 100 and 96 %, respectively, for the simulated data. SDH of different sizes is differentiated. The classifier requires training dataset size in order of 150 observations per class to achieve high accuracy. We conclude that the results indicate that MWT can detect and estimate the size of SDH. This is promising for developing MWT to be used for prehospital diagnosis of intracranial bleedings.
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| 3. |
- Harrison, Josie, 1989, et al.
(författare)
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Scalability in Building Component Data Annotation: Enhancing Facade Material Classification with Synthetic Data
- 2024
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Ingår i: Proceedings of the European Conference on Computing in Construction. - 2684-1150.
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Konferensbidrag (refereegranskat)abstract
- Computer vision models trained on Google Street View images can create material cadastres. However, current approaches need manually annotated datasets that are difficult to obtain and often have class imbalance. To address these challenges, this paper fine-tuned a Swin Transformer model on a synthetic dataset generated with OpenAI’s DALL E and compared the performance to a similar manually annotated dataset. Although manual annotation remains the gold standard, the synthetic dataset performance demonstrates a reasonable alternative. The findings will ease annotation needed to develop material cadastres, offering architects insights into opportunities for material reuse, thus contributing to the reduction of demolition waste.
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| 4. |
- Somanath, Sanjay, 1994, et al.
(författare)
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AI-baserad segementering av fasader för att optimera renovering i en större skala
- 2021
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Ingår i: Bygg och teknik. - 0281-658X. ; 2021:2, s. 26-29
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Hur kan vi på ett automatiskt sätt skapa mer detaljerade 3D modeller av byggnader i digitala tvillingar och förbättra indata för att beräkna energibesparingspotentialer i befintliga byggnader? I en pilotstudie har vi undersökt hur maskininlärning kan användas för att extrahera information om fönstersättning och storlek i befintliga byggnader. Vi har utvecklat en modell som har “tränats” att känna igen och segmenterar fönster från bilder med byggnadsfasader och på så sätt skapa digitala och mer detaljerade data för befintliga byggnader. Vårt långsiktiga mål är att utveckla en helautomatisk metod för analyser av renoveringspotentialer för byggnader och fastighetsportföljer.
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| 6. |
- Yu, Yinan, 1985, et al.
(författare)
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climateBUG: A data-driven framework for analyzing bank reporting through a climate lens
- 2024
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Ingår i: Expert Systems with Applications. - 0957-4174 .- 1873-6793. ; 239
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Tidskriftsartikel (refereegranskat)abstract
- This paper applies computational linguistics learning methods to the banking industry and climate change fields. We introduce our data-driven framework, climateBUG, with the aim of detecting latent information about how banks discuss their activities related to climate change using natural language processing (NLP). This framework consists of an ingestion pipeline, a configurable database, and a set of API’s. In addition, climateBUG offers two standalone components, namely a unique annotated corpus of approximately 1.1M statements from EU banks’ annual and sustainability reporting and a deep learning model adapted to the semantics of the corpus. When benchmarking on classification performance, our model outperforms other models with similar scopes due to its stronger domain relevance. We also provide examples of how the framework can be applied from a user perspective.
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| 7. |
- Yu, Yinan, 1985, et al.
(författare)
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Qually: A Quality Validation Toolbox for Automotive Perception Data Towards Trustworthy AI
- 2022
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Data-driven techniques such as artificial intelligence (AI) and deep learning are frequently deployed as part of automotive perception systems. Due to their heavy dependency on data, data quality is at the essence. In particular, in an automotive perception system, data is captured by sensors and transformed into different formats depending on where it is in the AI data processing pipeline. Although data at different stages share similar attributes, the impact of their properties at each individual stage differ significantly from one another. Therefore, data quality requirements need to be defined specifically at each stage. In this project, the objective is to develop an end-to-end quality control toolbox to detect errors and anomalies throughout the entire pipeline. To achieve this objective, we divide the project into three work packages, where the first step is to design a set of data properties and their corresponding requirements as quality specifications for data at each stage. Given these specifications, as a second step, we have developed a toolbox, Qually, to evaluate data quality metrics and detect errors and anomalies throughout the AI pipeline. In the last work package, as a demonstrator, Qually is applied to improve automated annotations. This is implemented in three steps: 1) errors are identified using the quality metrics evaluated by Qually; 2) Qually suggests an automatic correction using ensemble techniques; 3) the corrected annotations are evaluated by Qually to confirm the improvement in quality. The error detection and suggested corrections are manually inspected to statistically validate the outcome of Qually. As the next step, besides further developing Qually as a software to improve its robustness, capacity, scalability and completeness, we plan to focus on enriching the set of data properties and quality specifications, especially by including technical and business requirements from various automotive stakeholders. We also plan to investigate the possibility and scalability of integrating formal verification techniques for quality control.
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| 9. |
- Candefjord, Stefan, 1981, et al.
(författare)
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Microwave technology for localization of traumatic intracranial bleedings—a numerical simulation study
- 2013
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Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - 1557-170X. - 9781457702167 ; , s. 1948-1951
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Konferensbidrag (refereegranskat)abstract
- Traumatic brain injury (TBI) is a major public health problem worldwide. Intracranial bleedings represents the most serious complication of TBI and need to be surgically evacuated promptly to save lives and mitigate injury. Microwave technology (MWT) is promising as a complement to computed tomography (CT) to be used in road and air ambulances for early detection of intracranial bleedings. In this study, we perform numerical simulations to investigate if a classification algorithm based on singular value decomposition can distinguish between bleedings at different positions adjacent to the skull bone for a similar but simplified problem. The classification accuracy is 94-100% for all classes, a result that encourages us to pursue our efforts with MWT for more realistic scenarios. This indicates that MWT has potential for localizing a detected bleeding, which would increase the diagnostic value of this technique.
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