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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Radiomic machine learning for pretreatment assessment of prognostic risk factors for endometrial cancer and its effects on radiologists’ decisions of deep myometrial invasion / 子宮体癌の予後リスク因子の術前評価における機械学習を用いたRadiomics解析、およびその放射線科医の筋層浸潤評価に与える影響

Otani, Satoshi 23 March 2022 (has links)
京都大学 / 新制・課程博士 / 博士(医学) / 甲第23778号 / 医博第4824号 / 新制||医||1057(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 黒田 知宏, 教授 溝脇 尚志, 教授 波多野 悦朗 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM
2

A Study on Private and Secure Federated Learning / プライベートで安全な連合学習

Kato, Fumiyuki 25 March 2024 (has links)
京都大学 / 新制・課程博士 / 博士(情報学) / 甲第25427号 / 情博第865号 / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 伊藤 孝行, 教授 黒田 知宏, 教授 岡部 寿男, 吉川 正俊(京都大学 名誉教授) / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM
3

ADVANCED PRIOR MODELS FOR ULTRA SPARSE VIEW TOMOGRAPHY

Maliha Hossain (17014278) 26 September 2023 (has links)
<p dir="ltr">There is a growing need to reconstruct high quality tomographic images from sparse view measurements to accommodate time and space constraints as well as patient well-being in medical CT. Analytical methods perform poorly with sub-Nyquist acquisition rates. In extreme cases with 4 or fewer views, effective reconstruction approaches must be able to incorporate side information to constrain the solution space of an otherwise under-determined problem. This thesis presents two sparse view tomography problems that are solved using techniques that exploit. knowledge of the structural and physical properties of the scanned objects.</p><p dir="ltr"><br></p><p dir="ltr">First, we reconstruct four view CT datasets obtained from an in-situ imaging system used to observe Kolsky bar impact experiments. Test subjects are typically 3D-printed out ofhomogeneous materials into shapes with circular cross sections. Two advanced prior modelsare formulated to incorporate these assumptions in a modular fashion into the iterativeradiographic inversion framework. The first is a Multi-Slice Fusion and the latter is TotalVariation regularization that operates in cylindrical coordinates.</p><p dir="ltr"><br></p><p dir="ltr">In the second problem, artificial neural networks (NN) are used to directly invert a temporal sequence of four radiographic images of discontinuities propagating through an imploding steel shell. The NN is fed the radiographic features that are robust to scatter and is trained using density simulations synthesized as solutions to hydrodynamic equations of state. The proposed reconstruction pipeline learns and enforces physics-based assumptions of hydrodynamics and shock physics to constrain the final reconstruction to a space ofphysically admissible solutions.</p>

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