BAYES-MIL: A NEW PROBABILISTIC PERSPECTIVE
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Antoni B. Chan | Tei-Wei Kuo | Xue Liu | Yufei Cui | Ziquan Liu | Chun Jason Xue | Xiangyu Liu | Cong Wang
[1] R. G. Krishnan,et al. Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Yitian Zhao,et al. DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Xiangyang Ji,et al. TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classication , 2021, NeurIPS.
[4] Dimitris Samaras,et al. A Joint Spatial and Magnification Based Attention Framework for Large Scale Histopathology Classification , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[5] Antoni B. Chan,et al. Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[6] K. Eliceiri,et al. Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[7] R. Socher,et al. Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains , 2020, Nature Communications.
[8] Michael W. Dusenberry,et al. Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors , 2020, ICML.
[9] Ming Y. Lu,et al. Data-efficient and weakly supervised computational pathology on whole-slide images , 2020, Nature Biomedical Engineering.
[10] Dustin Tran,et al. BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning , 2020, ICLR.
[11] Thomas J. Fuchs,et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images , 2019, Nature Medicine.
[12] Qiao Li,et al. Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex , 2019, ArXiv.
[13] Hai Su,et al. Pathologist-level interpretable whole-slide cancer diagnosis with deep learning , 2019, Nat. Mach. Intell..
[14] Andrey Malinin,et al. Ensemble Distribution Distillation , 2019, ICLR.
[15] Shaoqun Zeng,et al. From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge , 2019, IEEE Transactions on Medical Imaging.
[16] Roberto Cipolla,et al. Convolutional CRFs for Semantic Segmentation , 2018, BMVC.
[17] Mark J. F. Gales,et al. Predictive Uncertainty Estimation via Prior Networks , 2018, NeurIPS.
[18] Max Welling,et al. Attention-based Deep Multiple Instance Learning , 2018, ICML.
[19] Andrew H. Beck,et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.
[20] Alex Kendall,et al. Concrete Dropout , 2017, NIPS.
[21] Alex Kendall,et al. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? , 2017, NIPS.
[22] Zoubin Ghahramani,et al. Deep Bayesian Active Learning with Image Data , 2017, ICML.
[23] Dmitry P. Vetrov,et al. Variational Dropout Sparsifies Deep Neural Networks , 2017, ICML.
[24] Diederik P. Kingma,et al. Variational Dropout and the Local Reparameterization Trick , 2015, NIPS.
[25] Vibhav Vineet,et al. Conditional Random Fields as Recurrent Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[26] Zoubin Ghahramani,et al. Bayesian Active Learning for Classification and Preference Learning , 2011, ArXiv.
[27] Yarin Gal,et al. Uncertainty in Deep Learning , 2016 .