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Slideflow: Deep Learning for Digital Histopathology with Real-Time
  Whole-Slide Visualization

Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization

9 April 2023
J. Dolezal
S. Kochanny
E. Dyer
Andrew Srisuwananukorn
Matteo Sacco
Frederick M. Howard
Anran Li
Prajval Mohan
Alexander T. Pearson
    MedIm
ArXivPDFHTML

Papers citing "Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualization"

4 / 4 papers shown
Title
Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images
Xiaoge Zhang
Tao Wang
Chao Yan
Fedaa Najdawi
Kai Zhou
Yuan Ma
Yiu-ming Cheung
Bradley Malin
MedIm
35
0
0
03 Jan 2025
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter
  Optimization
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Marius Lindauer
Katharina Eggensperger
Matthias Feurer
André Biedenkapp
Difan Deng
C. Benjamins
Tim Ruhopf
René Sass
Frank Hutter
83
325
0
20 Sep 2021
A disciplined approach to neural network hyper-parameters: Part 1 --
  learning rate, batch size, momentum, and weight decay
A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay
L. Smith
191
1,019
0
26 Mar 2018
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
261
9,134
0
06 Jun 2015
1