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Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training.

J. Valanarasu, Y. Tang, D. Yang, Z. Xu, C. Zhao, W. Li, V. Patel, B. Landman, D. Xu, Y. He, and V. Nath. CoRR, (2023)

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Martina Nath

Richa Nath

Jens Näth

Miriam Nath

Other publications of authors with the same name

Semi-supervised Machine Learning with MixMatch and Equivalence Classes.C. Hansen, V. Nath, R. Gao, C. Bermudez, Y. Huo, K. Sandler, P. Massion, J. Blume, T. Lasko, and B. Landman. iMIMIC/MIL3ID/LABELS@MICCAI, volume 12446 of Lecture Notes in Computer Science, page 112-121. Springer, (2020)Communication-Efficient Vertical Federated Learning with Limited Overlapping Samples.J. Sun, Z. Xu, D. Yang, V. Nath, W. Li, C. Zhao, D. Xu, Y. Chen, and H. Roth. ICCV, page 5180-5189. IEEE, (2023)HoloHisto: End-to-end Gigapixel WSI Segmentation with 4K Resolution Sequential Tokenization.Y. Tang, Y. He, V. Nath, P. Guo, R. Deng, T. Yao, Q. Liu, C. Cui, M. Yin, Z. Xu and 4 other author(s). CoRR, (2024)VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge.V. Nath, W. Li, D. Yang, A. Myronenko, M. Zheng, Y. Lu, Z. Liu, H. Yin, Y. Law, Y. Tang and 12 other author(s). CoRR, (2024)HyperSegNAS: Bridging One-Shot Neural Architecture Search with 3D Medical Image Segmentation using HyperNet.C. Peng, A. Myronenko, A. Hatamizadeh, V. Nath, M. Siddiquee, Y. He, D. Xu, R. Chellappa, and D. Yang. CVPR, page 20709-20719. IEEE, (2022)Learning 3D White Matter Microstructure from 2D Histology.V. Nath, K. Schilling, S. Remedios, R. Bayrak, Y. Gao, J. Blaber, Y. Huo, B. Landman, and A. Anderson. ISBI, page 186-190. IEEE, (2019)A Unified Learning Model for Estimating Fiber Orientation Distribution Functions on Heterogeneous Multi-shell Diffusion-Weighted MRI.T. Yao, N. Newlin, P. Kanakaraj, V. Nath, L. Cai, K. Ramadass, K. Schilling, B. Landman, and Y. Huo. CDMRI@MICCAI, volume 14328 of Lecture Notes in Computer Science, page 13-22. Springer, (2023)Warm Start Active Learning with Proxy Labels and Selection via Semi-supervised Fine-Tuning.V. Nath, D. Yang, H. Roth, and D. Xu. MICCAI (8), volume 13438 of Lecture Notes in Computer Science, page 297-308. Springer, (2022)The Power of Proxy Data and Proxy Networks for Hyper-parameter Optimization in Medical Image Segmentation.V. Nath, D. Yang, A. Hatamizadeh, A. Abidin, A. Myronenko, H. Roth, and D. Xu. MICCAI (3), volume 12903 of Lecture Notes in Computer Science, page 456-465. Springer, (2021)Enabling Multi-shell b-Value Generalizability of Data-Driven Diffusion Models with Deep SHORE.V. Nath, I. Lyu, K. Schilling, P. Parvathaneni, C. Hansen, Y. Huo, V. Janve, Y. Gao, I. Stepniewska, A. Anderson and 1 other author(s). MICCAI (3), volume 11766 of Lecture Notes in Computer Science, page 573-581. Springer, (2019)

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Disambiguation of "Nath, Vishwesh"

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Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training.

Please choose a person to relate this publication to

Martina Nath

Richa Nath

Jens Näth

Miriam Nath

Miriam Nath

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Nath, Vishwesh"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training.

Please choose a person to relate this publication to

Martina Nath

Richa Nath

Jens Näth

Miriam Nath

Miriam Nath

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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URL
DOI
BibTeX
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Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training.