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  1. Article ; Online: Determination of output factor for CyberKnife using scintillation dosimetry and deep learning.

    Ocampo, Jeremy / Heyes, Geoff / Dehghani, Hamid / Scanlon, Tim / Jolly, Simon / Gibson, Adam

    Physics in medicine and biology

    2024  Volume 69, Issue 2

    Abstract: ... ...

    Abstract Objective
    MeSH term(s) Deep Learning ; Radiometry/methods ; Radiosurgery/methods ; Algorithms ; Neural Networks, Computer
    Language English
    Publishing date 2024-01-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 208857-5
    ISSN 1361-6560 ; 0031-9155
    ISSN (online) 1361-6560
    ISSN 0031-9155
    DOI 10.1088/1361-6560/ad1b69
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions

    Ocampo, Jeremy / Price, Matthew A. / McEwen, Jason D.

    2022  

    Abstract: No existing spherical convolutional neural network (CNN) framework is both computationally scalable and rotationally equivariant. Continuous approaches capture rotational equivariance but are often prohibitively computationally demanding. Discrete ... ...

    Abstract No existing spherical convolutional neural network (CNN) framework is both computationally scalable and rotationally equivariant. Continuous approaches capture rotational equivariance but are often prohibitively computationally demanding. Discrete approaches offer more favorable computational performance but at the cost of equivariance. We develop a hybrid discrete-continuous (DISCO) group convolution that is simultaneously equivariant and computationally scalable to high-resolution. While our framework can be applied to any compact group, we specialize to the sphere. Our DISCO spherical convolutions exhibit $\text{SO}(3)$ rotational equivariance, where $\text{SO}(n)$ is the special orthogonal group representing rotations in $n$-dimensions. When restricting rotations of the convolution to the quotient space $\text{SO}(3)/\text{SO}(2)$ for further computational enhancements, we recover a form of asymptotic $\text{SO}(3)$ rotational equivariance. Through a sparse tensor implementation we achieve linear scaling in number of pixels on the sphere for both computational cost and memory usage. For 4k spherical images we realize a saving of $10^9$ in computational cost and $10^4$ in memory usage when compared to the most efficient alternative equivariant spherical convolution. We apply the DISCO spherical CNN framework to a number of benchmark dense-prediction problems on the sphere, such as semantic segmentation and depth estimation, on all of which we achieve the state-of-the-art performance.

    Comment: 19 pages, 7 figures, accepted by ICLR 2023
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2022-09-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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