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From Modern CNNs to Vision Transformers: Assessing the Performance,
  Robustness, and Classification Strategies of Deep Learning Models in
  Histopathology

From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology

11 April 2022
Maximilian Springenberg
A. Frommholz
M. Wenzel
Eva Weicken
Jackie Ma
Nils Strodthoff
    MedIm
ArXivPDFHTML

Papers citing "From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology"

4 / 4 papers shown
Title
Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection
Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection
Amine Bechar
Y. Elmir
Rafik Medjoudj
Yassine Himeur
Abbes Amira
ViT
MedIm
26
5
0
16 Nov 2023
Robust and Efficient Medical Imaging with Self-Supervision
Robust and Efficient Medical Imaging with Self-Supervision
Shekoofeh Azizi
Laura J. Culp
Jan Freyberg
Basil Mustafa
Sebastien Baur
...
Geoffrey E. Hinton
N. Houlsby
Alan Karthikesalingam
Mohammad Norouzi
Vivek Natarajan
OOD
63
58
0
19 May 2022
Bottleneck Transformers for Visual Recognition
Bottleneck Transformers for Visual Recognition
A. Srinivas
Tsung-Yi Lin
Niki Parmar
Jonathon Shlens
Pieter Abbeel
Ashish Vaswani
SLR
270
973
0
27 Jan 2021
Methods for Interpreting and Understanding Deep Neural Networks
Methods for Interpreting and Understanding Deep Neural Networks
G. Montavon
Wojciech Samek
K. Müller
FaML
234
2,233
0
24 Jun 2017
1