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  • MULTI-VIEW DEEP EVIDENTIAL FUSION NEURAL NETWORK FOR ASSESSMENT OF . . .
    Our results show that the multi-view assessment of mammograms using a deep evidential fusion approach not only provides superior performance than the single-view assessment but also enhances trust in incorporating artificial intelligence-powered algorithms for the assessment of screening mammograms
  • Multi-View Attention Network to Improve Breast Cancer Detection
    The experiment results showed that the detection performance of a high-resolution, multi-view attention network with an HRNet backbone was better than the other networks with dif-ferent configurations, suggesting that multi-view attention has benefits in detecting masses on mammograms
  • Naga Raju Gudhe - OpenReview
    22 Sept 2023 (modified: 10 Feb 2024) Submitted to ICLR 2024 MULTI-VIEW DEEP EVIDENTIAL FUSION NEURAL NETWORK FOR ASSESSMENT OF SCREENING MAMMOGRAMS Naga Raju Gudhe, Mazen Sudah, Arto Mannermaa, Veli-Matti Kosma, Hamid Behravan 22 Sept 2022 (modified: 13 Feb 2023) ICLR 2023 Conference Withdrawn Submission
  • Login - OpenReview
    Promoting openness in scientific communication and the peer-review process
  • TRUSTED MULTI-VIEW CLASSIFICATION - OpenReview
    We propose a novel multi-view classification model aiming to provide trusted and inter-pretable (according to the uncertainty of each view) decisions in an effective and efficient way (without any additional computations and neural network changes), which introduces a new paradigm in multi-view classification
  • Building Trust in Decision with Conformalized Multi-view Deep . . .
    While multi-view deep learning demonstrates notable efec-tiveness in real-world scenarios, such developments have largely thrived within the closed-world assumption, which assumes an ideal scenario where data views are error-free and training aligns per-fectly with testing distributions
  • Evidence Regularization for Multimodal Deep Evidential Regression
    Deep evidential regression (DER) (Amini et al , 2020) and its multimodal version (Ma et al , 2021) have revolutionized uncertainty estimation by probabilistically estimating target distribution parameters and enhancing the separation of uncertainties but faced issues like zero-confidence in training
  • T M -VIEW CLASSIFICATION VIA EVOLU M -V FUSION - OpenReview
    The pseudo view generation is formulated as a population-based multi-view neural archi-tecture problem where the higher-quality views can be automatically selected and fused with selected some fusion operators from a candidate fusion operator set
  • Improving the Ability of Deep Neural Networks to Use Information. . .
    Paper Type: methodological development Abstract: In breast cancer screening, radiologists make the diagnosis based on images that are taken from two angles Inspired by this, we seek to improve the performance of deep neural networks applied to this task by encouraging the model to use information from both views of the breast





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