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An Active Learning Method for Diabetic Retinopathy Classification with
  Uncertainty Quantification
v1v2 (latest)

An Active Learning Method for Diabetic Retinopathy Classification with Uncertainty Quantification

Medical and Biological Engineering and Computing (MBEC), 2020
24 December 2020
Muhammad Ahtazaz Ahsan
A. Qayyum
Junaid Qadir
Adeel Razi
    BDL
ArXiv (abs)PDFHTMLGithub

Papers citing "An Active Learning Method for Diabetic Retinopathy Classification with Uncertainty Quantification"

3 / 3 papers shown
A review of uncertainty quantification in medical image analysis:
  probabilistic and non-probabilistic methods
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods
Ling Huang
S. Ruan
Yucheng Xing
Mengling Feng
404
59
0
09 Oct 2023
Source-free Active Domain Adaptation for Diabetic Retinopathy Grading
  Based on Ultra-wide-field Fundus Image
Source-free Active Domain Adaptation for Diabetic Retinopathy Grading Based on Ultra-wide-field Fundus Image
Jinye Ran
Guanghua Zhang
Ximei Zhang
Juan Xie
F. Xia
Hao Zhang
MedIm
261
0
0
19 Sep 2023
Trustworthy clinical AI solutions: a unified review of uncertainty
  quantification in deep learning models for medical image analysis
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
Benjamin Lambert
Florence Forbes
A. Tucholka
Senan Doyle
Harmonie Dehaene
M. Dojat
299
190
0
05 Oct 2022
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