TY - GEN
T1 - Joint Dermatological Lesion Classification and Confidence Modeling with Uncertainty Estimation
AU - Lee, Gun Hee
AU - Ko, Han Bin
AU - Lee, Seong Whan
N1 - Funding Information:
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Program (Korea University)).
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Deep learning has played a major role in the interpretation of dermoscopic images for detecting skin defects and abnormalities. However, current deep learning solutions for dermatological lesion analysis are typically limited in providing probabilistic predictions which highlights the importance of concerning uncertainties. This concept of uncertainty can provide a confidence level for each feature which prevents overconfident predictions with poor generalization on unseen data. In this paper, we propose an overall framework that jointly considers dermatological classification and uncertainty estimation together. The estimated confidence of each feature to avoid uncertain feature and undesirable shift, which are caused by environmental difference of input image, in the latent space is pooled from confidence network. Our qualitative results show that modeling uncertainties not only helps to quantify model confidence for each prediction but also helps classification layers to focus on confident features, therefore, improving the accuracy for dermatological lesion classification. We demonstrate the potential of the proposed approach in two state-of-the-art dermoscopic datasets (ISIC 2018 and ISIC 2019).
AB - Deep learning has played a major role in the interpretation of dermoscopic images for detecting skin defects and abnormalities. However, current deep learning solutions for dermatological lesion analysis are typically limited in providing probabilistic predictions which highlights the importance of concerning uncertainties. This concept of uncertainty can provide a confidence level for each feature which prevents overconfident predictions with poor generalization on unseen data. In this paper, we propose an overall framework that jointly considers dermatological classification and uncertainty estimation together. The estimated confidence of each feature to avoid uncertain feature and undesirable shift, which are caused by environmental difference of input image, in the latent space is pooled from confidence network. Our qualitative results show that modeling uncertainties not only helps to quantify model confidence for each prediction but also helps classification layers to focus on confident features, therefore, improving the accuracy for dermatological lesion classification. We demonstrate the potential of the proposed approach in two state-of-the-art dermoscopic datasets (ISIC 2018 and ISIC 2019).
KW - Automatic diagnosis
KW - Confidence modeling
KW - Lesion classification
KW - Uncertainty estimation
UR - http://www.scopus.com/inward/record.url?scp=85130287168&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-02444-3_17
DO - 10.1007/978-3-031-02444-3_17
M3 - Conference contribution
AN - SCOPUS:85130287168
SN - 9783031024436
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 234
EP - 246
BT - Pattern Recognition - 6th Asian Conference, ACPR 2021, Revised Selected Papers
A2 - Wallraven, Christian
A2 - Liu, Qingshan
A2 - Nagahara, Hajime
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th Asian Conference on Pattern Recognition, ACPR 2021
Y2 - 9 November 2021 through 12 November 2021
ER -