Illustration of the calculation of the error distance maps on the Second Harmonic Generation image shown in (Fig 1a). (a) One representative image with a variety of fibres. (b) Dis... by Youssef Arafat (21009293)

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Illustration of the datasets that will be used to compare the tracing algorithms. (a) Second Harmonic Generation Collagen (SHG). (b) Fluorescent Fibronectin (FF). (c) Breast Cancer... by Youssef Arafat (21009293)

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Illustration of the data augmentation approaches used to train the U-Net. (a) Original patch (b) Horizontally flipped (c) Vertically flipped (d) Rotation by 90 degrees (e) Gaussian... by Youssef Arafat (21009293)

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Illustration of the U-Net architecture used to segment the fibres. The architecture consists of three encoder levels and 3 decoder levels. by Youssef Arafat (21009293)

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Post scan feature estimates and confidence ratings. by Sam Hall-McMaster (10343795)

The genetic algorithm. by Viktoria Zemliak (19500335)

Traffic sign detection results of CCTSDB 2021. by Qian Shen (89639)

“…The model achieves precision on par with existing mainstream algorithms, while being simpler, significantly reducing computational requirements, and being more suitable for small target detection tasks. …”

Modelling <i>In vitro</i> Mutagenicity Using Multi-Task Deep Learning and REACH Data by Panagiotis G. Karamertzanis (1274853)

“…This study explored the possibility of using the inherent correlation between these <i>in vitro</i> assays to create multi-task deep learning models and examine if they outperform single-task models. …”

Modelling <i>In vitro</i> Mutagenicity Using Multi-Task Deep Learning and REACH Data by Panagiotis G. Karamertzanis (1274853)

“…This study explored the possibility of using the inherent correlation between these <i>in vitro</i> assays to create multi-task deep learning models and examine if they outperform single-task models. …”

Modelling <i>In vitro</i> Mutagenicity Using Multi-Task Deep Learning and REACH Data by Panagiotis G. Karamertzanis (1274853)

“…This study explored the possibility of using the inherent correlation between these <i>in vitro</i> assays to create multi-task deep learning models and examine if they outperform single-task models. …”

Modelling <i>In vitro</i> Mutagenicity Using Multi-Task Deep Learning and REACH Data by Panagiotis G. Karamertzanis (1274853)

“…This study explored the possibility of using the inherent correlation between these <i>in vitro</i> assays to create multi-task deep learning models and examine if they outperform single-task models. …”

Modelling <i>In vitro</i> Mutagenicity Using Multi-Task Deep Learning and REACH Data by Panagiotis G. Karamertzanis (1274853)

“…This study explored the possibility of using the inherent correlation between these <i>in vitro</i> assays to create multi-task deep learning models and examine if they outperform single-task models. …”

Modelling <i>In vitro</i> Mutagenicity Using Multi-Task Deep Learning and REACH Data by Panagiotis G. Karamertzanis (1274853)

“…This study explored the possibility of using the inherent correlation between these <i>in vitro</i> assays to create multi-task deep learning models and examine if they outperform single-task models. …”

Modelling <i>In vitro</i> Mutagenicity Using Multi-Task Deep Learning and REACH Data by Panagiotis G. Karamertzanis (1274853)

“…This study explored the possibility of using the inherent correlation between these <i>in vitro</i> assays to create multi-task deep learning models and examine if they outperform single-task models. …”

Modelling <i>In vitro</i> Mutagenicity Using Multi-Task Deep Learning and REACH Data by Panagiotis G. Karamertzanis (1274853)

“…This study explored the possibility of using the inherent correlation between these <i>in vitro</i> assays to create multi-task deep learning models and examine if they outperform single-task models. …”

Modelling <i>In vitro</i> Mutagenicity Using Multi-Task Deep Learning and REACH Data by Panagiotis G. Karamertzanis (1274853)

“…This study explored the possibility of using the inherent correlation between these <i>in vitro</i> assays to create multi-task deep learning models and examine if they outperform single-task models. …”

Modelling <i>In vitro</i> Mutagenicity Using Multi-Task Deep Learning and REACH Data by Panagiotis G. Karamertzanis (1274853)

“…This study explored the possibility of using the inherent correlation between these <i>in vitro</i> assays to create multi-task deep learning models and examine if they outperform single-task models. …”

Modelling <i>In vitro</i> Mutagenicity Using Multi-Task Deep Learning and REACH Data by Panagiotis G. Karamertzanis (1274853)

“…This study explored the possibility of using the inherent correlation between these <i>in vitro</i> assays to create multi-task deep learning models and examine if they outperform single-task models. …”

Modelling <i>In vitro</i> Mutagenicity Using Multi-Task Deep Learning and REACH Data by Panagiotis G. Karamertzanis (1274853)

“…This study explored the possibility of using the inherent correlation between these <i>in vitro</i> assays to create multi-task deep learning models and examine if they outperform single-task models. …”

Modelling <i>In vitro</i> Mutagenicity Using Multi-Task Deep Learning and REACH Data by Panagiotis G. Karamertzanis (1274853)

“…This study explored the possibility of using the inherent correlation between these <i>in vitro</i> assays to create multi-task deep learning models and examine if they outperform single-task models. …”