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</div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.17033">arXiv:2305.17033</a> <span> [<a href="https://arxiv.org/pdf/2305.17033">pdf</a>, <a href="https://arxiv.org/format/2305.17033">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Image and Video Processing">eess.IV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Methods">q-bio.QM</span> </div> </div> <p class="title is-5 mathjax"> The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs) </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Kazerooni%2C+A+F">Anahita Fathi Kazerooni</a>, <a href="/search/q-bio?searchtype=author&query=Khalili%2C+N">Nastaran Khalili</a>, <a href="/search/q-bio?searchtype=author&query=Liu%2C+X">Xinyang Liu</a>, <a href="/search/q-bio?searchtype=author&query=Haldar%2C+D">Debanjan Haldar</a>, <a href="/search/q-bio?searchtype=author&query=Jiang%2C+Z">Zhifan Jiang</a>, <a href="/search/q-bio?searchtype=author&query=Anwar%2C+S+M">Syed Muhammed Anwar</a>, <a href="/search/q-bio?searchtype=author&query=Albrecht%2C+J">Jake Albrecht</a>, <a href="/search/q-bio?searchtype=author&query=Adewole%2C+M">Maruf Adewole</a>, <a href="/search/q-bio?searchtype=author&query=Anazodo%2C+U">Udunna Anazodo</a>, <a href="/search/q-bio?searchtype=author&query=Anderson%2C+H">Hannah Anderson</a>, <a href="/search/q-bio?searchtype=author&query=Bagheri%2C+S">Sina Bagheri</a>, <a href="/search/q-bio?searchtype=author&query=Baid%2C+U">Ujjwal Baid</a>, <a href="/search/q-bio?searchtype=author&query=Bergquist%2C+T">Timothy Bergquist</a>, <a href="/search/q-bio?searchtype=author&query=Borja%2C+A+J">Austin J. Borja</a>, <a href="/search/q-bio?searchtype=author&query=Calabrese%2C+E">Evan Calabrese</a>, <a href="/search/q-bio?searchtype=author&query=Chung%2C+V">Verena Chung</a>, <a href="/search/q-bio?searchtype=author&query=Conte%2C+G">Gian-Marco Conte</a>, <a href="/search/q-bio?searchtype=author&query=Dako%2C+F">Farouk Dako</a>, <a href="/search/q-bio?searchtype=author&query=Eddy%2C+J">James Eddy</a>, <a href="/search/q-bio?searchtype=author&query=Ezhov%2C+I">Ivan Ezhov</a>, <a href="/search/q-bio?searchtype=author&query=Familiar%2C+A">Ariana Familiar</a>, <a href="/search/q-bio?searchtype=author&query=Farahani%2C+K">Keyvan Farahani</a>, <a href="/search/q-bio?searchtype=author&query=Haldar%2C+S">Shuvanjan Haldar</a>, <a href="/search/q-bio?searchtype=author&query=Iglesias%2C+J+E">Juan Eugenio Iglesias</a>, <a href="/search/q-bio?searchtype=author&query=Janas%2C+A">Anastasia Janas</a> , et al. (48 additional authors not shown) </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2305.17033v7-abstract-short" style="display: inline;"> Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCA… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.17033v7-abstract-full').style.display = 'inline'; document.getElementById('2305.17033v7-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.17033v7-abstract-full" style="display: none;"> Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.17033v7-abstract-full').style.display = 'none'; document.getElementById('2305.17033v7-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/cond-mat/0108425">arXiv:cond-mat/0108425</a> <span> [<a href="https://arxiv.org/pdf/cond-mat/0108425">pdf</a>, <a href="https://arxiv.org/ps/cond-mat/0108425">ps</a>, <a href="https://arxiv.org/format/cond-mat/0108425">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Disordered Systems and Neural Networks">cond-mat.dis-nn</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Biology">q-bio</span> </div> </div> <p class="title is-5 mathjax"> Neural Representation of Probabilistic Information </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Barber%2C+M+J">M. J. Barber</a>, <a href="/search/q-bio?searchtype=author&query=Clark%2C+J+W">J. W. Clark</a>, <a href="/search/q-bio?searchtype=author&query=Anderson%2C+C+H">C. H. Anderson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="cond-mat/0108425v1-abstract-short" style="display: inline;"> It has been proposed that populations of neurons process information in terms of probability density functions (PDFs) of analog variables. Such analog variables range, for example, from target luminance and depth on the sensory interface to eye position and joint angles on the motor output side. The requirement that analog variables must be processed leads inevitably to a probabilistic descripti… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cond-mat/0108425v1-abstract-full').style.display = 'inline'; document.getElementById('cond-mat/0108425v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="cond-mat/0108425v1-abstract-full" style="display: none;"> It has been proposed that populations of neurons process information in terms of probability density functions (PDFs) of analog variables. Such analog variables range, for example, from target luminance and depth on the sensory interface to eye position and joint angles on the motor output side. The requirement that analog variables must be processed leads inevitably to a probabilistic description, while the limited precision and lifetime of the neuronal processing units leads naturally to a population representation of information. We show how a time-dependent probability density, residing in a specified function space of dimension D, may be decoded from the neuronal activities in a population as a linear combination of certain decoding functions, with coefficients given by the N firing rates (generally with D << N). We show how the neuronal encoding process may be described by projecting a set of complementary encoding functions on the probability density, and passing the result through a rectifying nonlinear activation function. We show how both encoders and decoders may be determined by minimizing cost functions that quantify the inaccuracy of the representation. Expressing a given computation in terms of manipulation and transformation of probabilities, we show how this representation leads to a neural circuit that can carry out the required computation within a consistent Bayesian framework, with the synaptic weights being explicitly generated in terms of encoders, decoders, conditional probabilities, and priors. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cond-mat/0108425v1-abstract-full').style.display = 'none'; document.getElementById('cond-mat/0108425v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 August, 2001; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2001. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">23 pages, 6 figures, submitted to Neural Comput</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/cond-mat/0102426">arXiv:cond-mat/0102426</a> <span> [<a href="https://arxiv.org/pdf/cond-mat/0102426">pdf</a>, <a href="https://arxiv.org/ps/cond-mat/0102426">ps</a>, <a href="https://arxiv.org/format/cond-mat/0102426">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Disordered Systems and Neural Networks">cond-mat.dis-nn</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Quantitative Biology">q-bio</span> </div> </div> <p class="title is-5 mathjax"> Neural Propagation of Beliefs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/q-bio?searchtype=author&query=Barber%2C+M+J">M. J. Barber</a>, <a href="/search/q-bio?searchtype=author&query=Clark%2C+J+W">J. W. Clark</a>, <a href="/search/q-bio?searchtype=author&query=Anderson%2C+C+H">C. H. Anderson</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="cond-mat/0102426v2-abstract-short" style="display: inline;"> We continue to explore the hypothesis that neuronal populations represent and process analog variables in terms of probability density functions (PDFs). A neural assembly encoding the joint probability density over relevant analog variables can in principle answer any meaningful question about these variables by implementing the Bayesian rules of inference. Aided by an intermediate representatio… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cond-mat/0102426v2-abstract-full').style.display = 'inline'; document.getElementById('cond-mat/0102426v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="cond-mat/0102426v2-abstract-full" style="display: none;"> We continue to explore the hypothesis that neuronal populations represent and process analog variables in terms of probability density functions (PDFs). A neural assembly encoding the joint probability density over relevant analog variables can in principle answer any meaningful question about these variables by implementing the Bayesian rules of inference. Aided by an intermediate representation of the probability density based on orthogonal functions spanning an underlying low-dimensional function space, we show how neural circuits may be generated from Bayesian belief networks. The ideas and the formalism of this PDF approach are illustrated and tested with several elementary examples, and in particular through a problem in which model-driven top-down information flow influences the processing of bottom-up sensory input. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('cond-mat/0102426v2-abstract-full').style.display = 'none'; document.getElementById('cond-mat/0102426v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 28 August, 2001; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 February, 2001; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2001. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">22 pages, 5 figures, submitted to Neural Computation, v2. significant structural changes</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 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