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Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment.

D. Ho, N. Agaram, P. Schüffler, C. Vanderbilt, M. Jean, M. Hameed, and T. Fuchs. MICCAI (5), volume 12265 of Lecture Notes in Computer Science, page 540-549. Springer, (2020)

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Chad Davis

Adrian Chadi

Lore Chadmand

Marcin Chady

Other publications of authors with the same name

Beyond Classification: Whole Slide Tissue Histopathology Analysis By End-To-End Part Learning.C. Xie, H. Muhammad, C. Vanderbilt, R. Caso, D. Yarlagadda, G. Campanella, and T. Fuchs. MIDL, volume 121 of Proceedings of Machine Learning Research, page 843-856. PMLR, (2020)H&E-based Computational Biomarker Enables Universal EGFR Screening for Lung Adenocarcinoma.G. Campanella, D. Ho, I. Häggström, A. Becker, J. Chang, C. Vanderbilt, and T. Fuchs. CoRR, (2022)Computational Pathology at Health System Scale - Self-Supervised Foundation Models from Three Billion Images.G. Campanella, R. Kwan, E. Fluder, J. Zeng, A. Stock, B. Veremis, A. Polydorides, C. Hedvat, A. Schoenfeld, C. Vanderbilt and 3 other author(s). CoRR, (2023)Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction.D. Ho, N. Agaram, M. Jean, S. Suser, C. Chu, C. Vanderbilt, P. Meyers, L. Wexler, J. Healey, T. Fuchs and 1 other author(s). CoRR, (2022)Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment.D. Ho, N. Agaram, P. Schüffler, C. Vanderbilt, M. Jean, M. Hameed, and T. Fuchs. MICCAI (5), volume 12265 of Lecture Notes in Computer Science, page 540-549. Springer, (2020)Beyond Multiple Instance Learning: Full Resolution All-In-Memory End-To-End Pathology Slide Modeling.G. Campanella, E. Fluder, J. Zeng, C. Vanderbilt, and T. Fuchs. CoRR, (2024)VOCA: Cell Nuclei Detection In Histopathology Images By Vector Oriented Confidence Accumulation.C. Xie, C. Vanderbilt, A. Grabenstetter, and T. Fuchs. MIDL, volume 102 of Proceedings of Machine Learning Research, page 527-539. PMLR, (2019)Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center.P. Schüffler, L. Geneslaw, D. Yarlagadda, M. Hanna, J. Samboy, E. Stamelos, C. Vanderbilt, J. Philip, M. Jean, L. Corsale and 22 other author(s). J. Am. Medical Informatics Assoc., 28 (9): 1874-1884 (2021)Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation.D. Ho, M. Chui, C. Vanderbilt, J. Jung, M. Robson, C. Park, J. Roh, and T. Fuchs. CoRR, (2022)Nuc2Vec: Learning Representations of Nuclei in Histopathology Images with Contrastive Loss.C. Feng, C. Vanderbilt, and T. Fuchs. MIDL, volume 143 of Proceedings of Machine Learning Research, page 179-189. PMLR, (2021)

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Disambiguation of "Vanderbilt, Chad M."

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Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment.

Please choose a person to relate this publication to

Chad Davis

Adrian Chadi

Lore Chadmand

Lore Chadmand

Marcin Chady

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Vanderbilt, Chad M."

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment.

Please choose a person to relate this publication to

Chad Davis

Adrian Chadi

Lore Chadmand

Lore Chadmand

Marcin Chady

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
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APA
Chicago
DIN 1505
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Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment.