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A Generalized Deep Learning Framework for Whole-Slide Image Segmentation
  and Analysis

A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis

1 January 2020
Mahendra Khened
Avinash Kori
Haran Rajkumar
Balaji Srinivasan
Ganapathy Krishnamurthi
    MedIm
    LM&MA
ArXivPDFHTML

Papers citing "A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis"

6 / 6 papers shown
Title
When Medical Imaging Met Self-Attention: A Love Story That Didn't Quite
  Work Out
When Medical Imaging Met Self-Attention: A Love Story That Didn't Quite Work Out
Tristan Piater
Niklas Penzel
Gideon Stein
Joachim Denzler
34
2
0
18 Apr 2024
Semi-supervised ViT knowledge distillation network with style transfer
  normalization for colorectal liver metastases survival prediction
Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction
Mohamed El Amine Elforaici
E. Montagnon
Francisco Perdigon Romero
W. Le
F. Azzi
Dominique Trudel
Bich Nguyen
Simon Turcotte
An Tang
Samuel Kadoury
MedIm
18
2
0
17 Nov 2023
Efficient Segmentation with Texture in Ore Images Based on
  Box-supervised Approach
Efficient Segmentation with Texture in Ore Images Based on Box-supervised Approach
Guodong Sun
Delong Huang
Yuting Peng
Lei Cheng
Bo Wu
Yang Zhang
22
2
0
10 Nov 2023
Variability Matters : Evaluating inter-rater variability in
  histopathology for robust cell detection
Variability Matters : Evaluating inter-rater variability in histopathology for robust cell detection
Cholmin Kang
C. Lee
Heon Song
M. Ma
Sérgio Pereira
10
5
0
11 Oct 2022
Glo-In-One: Holistic Glomerular Detection, Segmentation, and Lesion
  Characterization with Large-scale Web Image Mining
Glo-In-One: Holistic Glomerular Detection, Segmentation, and Lesion Characterization with Large-scale Web Image Mining
Tianyuan Yao
Yuzhe Lu
Jun Long
Aadarsh Jha
Zheyu Zhu
Zuhayr Asad
Haichun Yang
Agnes B. Fogo
Yuankai Huo
12
9
0
31 May 2022
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
247
9,042
0
06 Jun 2015
1