Chad M. Vanderbilt

According to our database1, Chad M. Vanderbilt authored at least 12 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspective.
CoRR, 2024

A Clinical Benchmark of Public Self-Supervised Pathology Foundation Models.
CoRR, 2024

Beyond Multiple Instance Learning: Full Resolution All-In-Memory End-To-End Pathology Slide Modeling.
CoRR, 2024

2023
Computational Pathology at Health System Scale - Self-Supervised Foundation Models from Three Billion Images.
CoRR, 2023

2022
Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction.
CoRR, 2022

H&E-based Computational Biomarker Enables Universal EGFR Screening for Lung Adenocarcinoma.
CoRR, 2022

Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation.
CoRR, 2022

2021
Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center.
J. Am. Medical Informatics Assoc., 2021

Nuc2Vec: Learning Representations of Nuclei in Histopathology Images with Contrastive Loss.
Proceedings of the Medical Imaging with Deep Learning, 7-9 July 2021, Lübeck, Germany., 2021

2020
Beyond Classification: Whole Slide Tissue Histopathology Analysis By End-To-End Part Learning.
Proceedings of the International Conference on Medical Imaging with Deep Learning, 2020

Deep Interactive Learning: An Efficient Labeling Approach for Deep Learning-Based Osteosarcoma Treatment Response Assessment.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

2019
VOCA: Cell Nuclei Detection In Histopathology Images By Vector Oriented Confidence Accumulation.
Proceedings of the International Conference on Medical Imaging with Deep Learning, 2019


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