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Interpretable and intervenable ultrasonography-based machine learning
  models for pediatric appendicitis
v1v2v3 (latest)

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
ArXiv (abs)PDFHTML

Papers citing "Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis"

19 / 19 papers shown
Title
Measuring Leakage in Concept-Based Methods: An Information Theoretic Approach
Measuring Leakage in Concept-Based Methods: An Information Theoretic Approach
Mikael Makonnen
Moritz Vandenhirtz
Sonia Laguna
Julia E. Vogt
86
3
0
13 Apr 2025
A survey of multimodal deep generative models
A survey of multimodal deep generative models
Masahiro Suzuki
Y. Matsuo
SyDaDRL
77
80
0
05 Jul 2022
GlanceNets: Interpretabile, Leak-proof Concept-based Models
GlanceNets: Interpretabile, Leak-proof Concept-based Models
Emanuele Marconato
Andrea Passerini
Stefano Teso
165
69
0
31 May 2022
Provable concept learning for interpretable predictions using
  variational autoencoders
Provable concept learning for interpretable predictions using variational autoencoders
Armeen Taeb
Nicolò Ruggeri
Carina Schnuck
Fanny Yang
119
5
0
01 Apr 2022
Generative Adversarial Networks
Generative Adversarial Networks
Gilad Cohen
Raja Giryes
GAN
298
30,152
0
01 Mar 2022
Promises and Pitfalls of Black-Box Concept Learning Models
Promises and Pitfalls of Black-Box Concept Learning Models
Anita Mahinpei
Justin Clark
Isaac Lage
Finale Doshi-Velez
Weiwei Pan
91
96
0
24 Jun 2021
Self-Supervised Learning with Data Augmentations Provably Isolates
  Content from Style
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
Julius von Kügelgen
Yash Sharma
Luigi Gresele
Wieland Brendel
Bernhard Schölkopf
M. Besserve
Francesco Locatello
110
317
0
08 Jun 2021
Do Concept Bottleneck Models Learn as Intended?
Do Concept Bottleneck Models Learn as Intended?
Andrei Margeloiu
Matthew Ashman
Umang Bhatt
Yanzhi Chen
M. Jamnik
Adrian Weller
SLR
56
97
0
10 May 2021
Concept Bottleneck Models
Concept Bottleneck Models
Pang Wei Koh
Thao Nguyen
Y. S. Tang
Stephen Mussmann
Emma Pierson
Been Kim
Percy Liang
101
835
0
09 Jul 2020
What Makes for Good Views for Contrastive Learning?
What Makes for Good Views for Contrastive Learning?
Yonglong Tian
Chen Sun
Ben Poole
Dilip Krishnan
Cordelia Schmid
Phillip Isola
SSL
118
1,338
0
20 May 2020
On Completeness-aware Concept-Based Explanations in Deep Neural Networks
On Completeness-aware Concept-Based Explanations in Deep Neural Networks
Chih-Kuan Yeh
Been Kim
Sercan O. Arik
Chun-Liang Li
Tomas Pfister
Pradeep Ravikumar
FAtt
293
307
0
17 Oct 2019
Cats or CAT scans: transfer learning from natural or medical image
  source datasets?
Cats or CAT scans: transfer learning from natural or medical image source datasets?
Veronika Cheplygina
OODMedIm
72
59
0
12 Oct 2018
Generative Image Inpainting with Contextual Attention
Generative Image Inpainting with Contextual Attention
Jiahui Yu
Zhe Lin
Jimei Yang
Xiaohui Shen
Xin Lu
Thomas S. Huang
GANDiffM
106
2,267
0
24 Jan 2018
Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad
  and the Ugly
Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Ugly
Yongqin Xian
Christoph H. Lampert
Bernt Schiele
Zeynep Akata
VLM
172
1,572
0
03 Jul 2017
Multimodal Machine Learning: A Survey and Taxonomy
Multimodal Machine Learning: A Survey and Taxonomy
T. Baltrušaitis
Chaitanya Ahuja
Louis-Philippe Morency
119
2,945
0
26 May 2017
Selective Classification for Deep Neural Networks
Selective Classification for Deep Neural Networks
Yonatan Geifman
Ran El-Yaniv
CVBM
101
530
0
23 May 2017
Reducing Overfitting in Deep Networks by Decorrelating Representations
Reducing Overfitting in Deep Networks by Decorrelating Representations
Michael Cogswell
Faruk Ahmed
Ross B. Girshick
C. L. Zitnick
Dhruv Batra
95
415
0
19 Nov 2015
A Survey on Multi-view Learning
A Survey on Multi-view Learning
Chang Xu
Dacheng Tao
Chao Xu
AI4TS
122
1,132
0
20 Apr 2013
d-Separation: From Theorems to Algorithms
d-Separation: From Theorems to Algorithms
D. Geiger
Thomas Verma
Judea Pearl
87
242
0
27 Mar 2013
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