Learning More with Less: GAN-based Medical Image Augmentation

Changhee, Han; Murao, Kohei; Satoh, Shin’ichi; Nakayama, Hideki

doi:10.48550/arxiv.1904.00838

Cited by 3 publications

(2 citation statements)

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“…Deep neural networks, which have multiple layers of perceptrons, are particularly well suited to visual tasks and have been applied to detecting and classifying objects in images, edge detection and semantic segmentation (Schmidhuber 2015). GANs have been particularly applied to the generation of new data, including the generation of videos and images (Huang 2018) and medical images (Han 2019). This makes them particularly interesting for architecture, as they address the problem of representation (Gero 1991) by allowing design knowledge to be represented and generated in the form of pixel images -a common representation within the architectural design process.…”

Section: ) Generative Adversarial Networkmentioning

confidence: 99%

Predicting and steering performance in architectural materials

Thomsen¹,

Nicholas²,

Tamke³

et al. 2019

Proceedings of the 37th International Conference on Education and Research in Computer Aided Architectural Design in Europe (eC

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This paper presents the prototyping of new methods by which functionally graded materials can be specified and produced. The paper presents a case study exploring how machine learning can be used to train a model in order to predict fabrication files from formalised design requirements. By using knit as a model for material fabrication, the paper outlines the making of new cyclical design methods employing machine learning in which simpler prototypical materials acts as input for more complex graded materials. A case study -Ombre -showcases the implementation of this workflow and results and perspectives are discussed.

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Section: ) Generative Adversarial Networkmentioning

confidence: 99%

Predicting and steering performance in architectural materials

Thomsen¹,

Nicholas²,

Tamke³

et al. 2019

Proceedings of the 37th International Conference on Education and Research in Computer Aided Architectural Design in Europe (eC

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“…The U-Net architecture [ 14 ] has been widely used used in these cases. This is because an important challenge in these applications is the ability to work with small datasets and with a limited amount of annotated samples, since generating additional samples is expensive and requires domain expertise [ 15 , 16 , 17 ], and the U-Net was designed with that in mind. Many variants have been proposed to fine-tune it for different applications [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Automatic Quantification Protocol for Biomarkers of Tauopathies in the Hippocampus and Entorhinal Cortex of Post-Mortem Samples Using an Extended Semi-Siamese U-Net

Campero-García

Cantoral-Ceballos

Martínez-Maldonado

et al. 2022

Biology

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Efforts have been made to diagnose and predict the course of different neurodegenerative diseases through various imaging techniques. Particularly tauopathies, where the tau polypeptide is a key participant in molecular pathogenesis, have significantly increased their morbidity and mortality in the human population over the years. However, the standard approach to exploring the phenomenon of neurodegeneration in tauopathies has not been directed at understanding the molecular mechanism that causes the aberrant polymeric and fibrillar behavior of the tau protein, which forms neurofibrillary tangles that replace neuronal populations in the hippocampal and cortical regions. The main objective of this work is to implement a novel quantification protocol for different biomarkers based on pathological post-translational modifications undergone by tau in the brains of patients with tauopathies. The quantification protocol consists of an adaptation of the U-Net neural network architecture. We used the resulting segmentation masks for the quantification of combined fluorescent signals of the different molecular changes tau underwent in neurofibrillary tangles. The quantification considers the neurofibrillary tangles as an individual study structure separated from the rest of the quadrant present in the images. This allows us to detect unconventional interaction signals between the different biomarkers. Our algorithm provides information that will be fundamental to understanding the pathogenesis of dementias with another computational analysis approach in subsequent studies.

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