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FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic
  Aberration Detection

FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration Detection

1 November 2024
Simon Gutwein
Martin Kampel
Sabine Taschner-Mandl
Roxane Licandro
    MedIm
ArXiv (abs)PDFHTMLGithub (1★)

Papers citing "FISHing in Uncertainty: Synthetic Contrastive Learning for Genetic Aberration Detection"

9 / 9 papers shown
Title
Probabilistic Contrastive Learning Recovers the Correct Aleatoric
  Uncertainty of Ambiguous Inputs
Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs
Michael Kirchhof
Enkelejda Kasneci
Seong Joon Oh
UQCV
485
25
0
06 Feb 2023
Uncertainty in Contrastive Learning: On the Predictability of Downstream
  Performance
Uncertainty in Contrastive Learning: On the Predictability of Downstream Performance
Shervin Ardeshir
Navid Azizan
SSLUQCV
88
9
0
19 Jul 2022
Mitigating Neural Network Overconfidence with Logit Normalization
Mitigating Neural Network Overconfidence with Logit Normalization
Hongxin Wei
Renchunzi Xie
Hao-Ran Cheng
Lei Feng
Bo An
Yixuan Li
OODD
223
285
0
19 May 2022
Deep Deterministic Uncertainty: A Simple Baseline
Deep Deterministic Uncertainty: A Simple Baseline
Jishnu Mukhoti
Andreas Kirsch
Joost R. van Amersfoort
Philip Torr
Y. Gal
UDUQCVPERBDL
96
155
0
23 Feb 2021
A Simple Framework for Contrastive Learning of Visual Representations
A Simple Framework for Contrastive Learning of Visual Representations
Ting-Li Chen
Simon Kornblith
Mohammad Norouzi
Geoffrey E. Hinton
SSL
378
18,866
0
13 Feb 2020
Image Classification with Deep Learning in the Presence of Noisy Labels:
  A Survey
Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey
G. Algan
ilkay Ulusoy
NoLaVLM
83
333
0
11 Dec 2019
UMAP: Uniform Manifold Approximation and Projection for Dimension
  Reduction
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Leland McInnes
John Healy
James Melville
195
9,473
0
09 Feb 2018
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCVBDL
842
5,841
0
05 Dec 2016
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
UQCVBDL
847
9,346
0
06 Jun 2015
1