Zobrazeno 1 - 10 of 33 pro vyhledávání: '"Kazuki Saruta"'
1
Akademický článek
Detection of presence or absence of metastasis in WSI patches of breast cancer using the dual-enhanced convolutional ensemble neural network
Autor: Ruigang Ge, Guoyue Chen, Kazuki Saruta, Yuki Terata
Publikováno v: Machine Learning with Applications, Vol 17, Iss , Pp 100579- (2024)
Breast cancer (BC) is a prevalent malignancy worldwide, posing a significant public health burden due to its high incidence rate. Accurate detection is crucial for improving survival rates, and pathological diagnosis through biopsy is essential for d
Externí odkaz: https://doaj.org/article/43083802c11a4f2cb26c6733c4d5369d
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2
Akademický článek
Tumor detection in breast cancer pathology patches using a Multi-scale Multi-head Self-attention Ensemble Network on Whole Slide Images
Autor: Ruigang Ge, Guoyue Chen, Kazuki Saruta, Yuki Terata
Publikováno v: Machine Learning with Applications, Vol 18, Iss , Pp 100592- (2024)
Breast cancer (BC) is the most common type of cancer among women globally and is one of the leading causes of cancer-related deaths among women. In the diagnosis of BC, histopathological assessment is the gold standard, where automated tumor detectio
Externí odkaz: https://doaj.org/article/08a63324a7d14b31bc4cd6c8df1aae1b
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3
Akademický článek
Structure Extraction With Total Variation for Hyperspectral Image Classification
Autor: Qiaoqiao Li, Haibo Wang, Guoyue Chen, Kazuki Saruta, Yuki Terata
Publikováno v: IEEE Access, Vol 7, Pp 91019-91033 (2019)
This paper proposes a novel structure extraction approach that is able to achieve high classification accuracy and low computing burden to hyperspectral image (HSI) classification based on total variation (SETV). Specifically, a two-scale decompositi
Externí odkaz: https://doaj.org/article/e790303579c642ddaf6f2d4cec862424
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4
Akademický článek
Semantic Segmentation of Agricultural Images Based on Style Transfer Using Conditional and Unconditional Generative Adversarial Networks
Autor: Hirokazu Madokoro, Kota Takahashi, Satoshi Yamamoto, Stephanie Nix, Shun Chiyonobu, Kazuki Saruta, Takashi K. Saito, Yo Nishimura, Kazuhito Sato
Publikováno v: Applied Sciences, Vol 12, Iss 15, p 7785 (2022)
Classification, segmentation, and recognition techniques based on deep-learning algorithms are used for smart farming. It is an important and challenging task to reduce the time, burden, and cost of annotation procedures for collected datasets from f
Externí odkaz: https://doaj.org/article/c30f0be50688405a8adcf86408899ca6
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7
Structure Extraction With Total Variation for Hyperspectral Image Classification
Autor: Haibo Wang, Kazuki Saruta, Guoyue Chen, Qiaoqiao Li, Yuki Terata
Publikováno v: IEEE Access, Vol 7, Pp 91019-91033 (2019)
This paper proposes a novel structure extraction approach that is able to achieve high classification accuracy and low computing burden to hyperspectral image (HSI) classification based on total variation (SETV). Specifically, a two-scale decompositi
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c8e02537c46d63f99b7f1b9ec45c1f1
https://ieeexplore.ieee.org/document/8736233/
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8
A Guideline for Object Detection Using Convolutional Neural Networks
Autor: Xingguo Zhang, Kazuki Saruta, Yuki Terata, Guoyue Chen
Publikováno v: Lecture Notes in Electrical Engineering ISBN: 9789811501869
The main purpose of object detection is to detect and locate specific targets from images. The traditional detection methods are usually complex and require prior knowledge of the detection target. In this paper, we will introduce how to use convolut
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::03a12a9750f37882a1b8620af6ead33f
https://doi.org/10.1007/978-981-15-0187-6_18
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