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2311.09574
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LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype
16 November 2023
V. Shankar
Xiaoli Yang
Vrishab Krishna
Brent Tan
Oscar Silva
Rebecca Rojansky
A. Ng
F. Valvert
Edward L Briercheck
D. Weinstock
Y. Natkunam
S. Fernandez-Pol
Pranav Rajpurkar
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Papers citing
"LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype"
7 / 7 papers shown
Title
Self-supervised driven consistency training for annotation efficient histopathology image analysis
C. Srinidhi
Seung Wook Kim
Fu-Der Chen
Anne L. Martel
SSL
52
110
0
07 Feb 2021
DLBCL-Morph: Morphological features computed using deep learning for an annotated digital DLBCL image set
Damir Vrabac
Akshay Smit
Rebecca Rojansky
Y. Natkunam
R. Advani
A. Ng
S. Fernandez-Pol
Pranav Rajpurkar
9
27
0
17 Sep 2020
HoVer-Net: Simultaneous Segmentation and Classification of Nuclei in Multi-Tissue Histology Images
S. Graham
Q. Vu
S. Raza
A. Azam
Yee Wah Tsang
J. T. Kwak
Nasir M. Rajpoot
56
1,011
0
16 Dec 2018
Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning
Hanadi El Achi
Tatiana Belousova
Lei Chen
Amer Wahed
Iris Wang
Z. Hu
Zeyad Kanaan
A. Rios
A. Nguyen
LM&MA
MedIm
23
73
0
30 Oct 2018
Cell Detection with Star-convex Polygons
Uwe Schmidt
Martin Weigert
Coleman Broaddus
E. Myers
52
1,096
0
09 Jun 2018
A Unified Approach to Interpreting Model Predictions
Scott M. Lundberg
Su-In Lee
FAtt
574
21,613
0
22 May 2017
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
1.4K
192,638
0
10 Dec 2015
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