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Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis
28 February 2023
Ricards Marcinkevics
Patricia Reis Wolfertstetter
Ugne Klimiene
Kieran Chin-Cheong
Alyssia Paschke
Julia Zerres
Markus Denzinger
David Niederberger
S. Wellmann
Ece Ozkan
C. Knorr
Julia E. Vogt
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Papers citing
"Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis"
6 / 6 papers shown
Title
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?
Sonia Laguna
Ricards Marcinkevics
Moritz Vandenhirtz
Julia E. Vogt
30
17
0
24 Jan 2024
A survey of multimodal deep generative models
Masahiro Suzuki
Y. Matsuo
SyDa
DRL
57
76
0
05 Jul 2022
GlanceNets: Interpretabile, Leak-proof Concept-based Models
Emanuele Marconato
Andrea Passerini
Stefano Teso
106
64
0
31 May 2022
Post-hoc Concept Bottleneck Models
Mert Yuksekgonul
Maggie Wang
James Y. Zou
145
185
0
31 May 2022
On Completeness-aware Concept-Based Explanations in Deep Neural Networks
Chih-Kuan Yeh
Been Kim
Sercan Ö. Arik
Chun-Liang Li
Tomas Pfister
Pradeep Ravikumar
FAtt
122
297
0
17 Oct 2019
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
251
3,683
0
28 Feb 2017
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