End-to-End Decoupled Training: A Robust Deep Learning Method for Long-Tailed Classification of Dermoscopic Images for Skin Lesion Classification
Abstract
:1. Introduction
- We propose two new loss functions, and , able to weight samples more efficiently so as to guide the network to focus on informative samples;
- We propose an approach able to handle both the class imbalance issue and the outlier issue;
- We propose a new learning scheme for the decoupled training following an end-to-end process;
- We demonstrate the strength of our method on the ISIC 2018 long-tail benchmark dataset and show improved performance over both existing methods that deal with the class imbalance problem and prior works on the same tasks.
2. Related Work
2.1. Design of CAD System for Skin Lesion Detection
2.1.1. CAD Based on One CNN
2.1.2. CAD Based on an Ensemble of CNN
2.1.3. CAD Based on CNNs Combined with Other Classifiers
2.2. Methods for Handling Long-Tail Distributions
2.2.1. Data-Level Approach
2.2.2. Classifier-Level Methods
2.2.3. Decoupled Training
3. Problem Setting and Analysis
3.1. Problem Setting
3.2. Analysis
4. Materials and Methodology
4.1. Theoretical Motivation
4.2. Definition of Loss Functions
- When the gradient of a sample was very large, corresponding to near 0, the loss went to 0, and the model was less affected by outliers.
- When the gradient of a sample was very low, corresponding to near 1, the loss went to 0, which prevented the model from being overwhelmed by easy samples.
4.3. Description of the Proposed Learning Framework
4.4. Dataset Description and Preparation
4.5. Training of the Convolutional Neural Network
4.6. Evaluation Metrics
5. Results
- We conducted an ablative study to analyze which of the commonly used loss function CE and was more appropriate for stage one;
- We compared our full pipeline with common methods in the literature proposed for handling class imbalance, namely cost-sensitive loss (CS) [38], class-balanced loss by effective number of classes (CB) [23], focal loss (FL) [22], label-distribution-aware margin loss (LDAM) [41], influence-balanced Loss (IB) [60], bag of tricks (BAGs) [50] and decoupled training [21];
- We compared our approach with prior works developing CAD systems for SL classification;
- We analyzed the best performance achieved with our pipelines.
5.1. Comparative Study of Our Approach with SOA Approach for Handling Class Imbalance
5.2. Performance of the Best Model with Our Approach
5.3. Comparative Study with Other CAD Systems for Skin Lesion Detection
5.4. Ablative Study
5.4.1. Effectiveness of Loss for the First Stage of Decoupled Training
5.4.2. Effectiveness of Our Learning Scheme Compared to a Conventional Scheme
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Block N° | Layer Name | Resolution | Filter Size | Number of Layers |
---|---|---|---|---|
1 | Conv | 300 × 300 | 3 × 3 | 1 |
2 | MBConv1 | 150 × 150 | 3 × 3 | 2 |
3 | MBConv6 | 150 × 150 | 5 × 5 | 3 |
4 | MBConv6 | 75 × 75 | 3 × 3 | 3 |
5 | MBConv6 | 38 × 38 | 3 × 3 | 5 |
6 | MBConv6 | 19 × 19 | 5 × 5 | 5 |
7 | MBConv6 | 10 × 10 | 5 × 5 | 6 |
8 | MBConv6 | 10 × 10 | 3 × 3 | 2 |
9 | Conv | 10 × 10 | 1 × 1 | 1 |
10 | Global pooling | 10 × 10 | 1 | |
11 | Dense layer | 10 × 10 | 1 |
Methods | Head | Medium | Tail | All |
---|---|---|---|---|
CS [38] | 0.76 ± 0.02 | 0.83 ± 0.02 | 0.95 ± 0.03 | 0.84 ± 0.01 |
CB [23] | 0.79 ± 0.01 | 0.86 ± 0.01 | 0.95 ± 0.04 | 0.85 ± 0.01 |
FL [22] | 0.80 ± 0.01 | 0.83 ± 0.05 | 0.88 ± 0.04 | 0.83 ± 0.01 |
LDAM [41] | 0.76 ± 0.03 | 0.78 ± 0.01 | 0.93 ± 0.04 | 0.82 ± 0.02 |
IB [60] | 0.83 ± 0.01 | 0.81 ± 0.04 | 0.87 ± 0.01 | 0.82 ± 0.02 |
BAGs [50] | 0.79 ± 0.01 | 0.84 ± 0.02 | 0.92 ± 0.02 | 0.85 ± 0.02 |
Decoupled learning [21] | 0.80 ± 0.01 | 0.82 ± 0.02 | 0.88 ± 0.02 | 0.83 ± 0.02 |
Our method | 0.81 ± 0.01 | 0.88 ± 0.02 | 0.98 ± 0.01 | 0.87 ± 0.01 |
Works | Methods | BACC |
---|---|---|
Al-masni et al. [62] | Single CNN | 0.81 |
Gessert et al. [10] | Ensemble of CNNs | 0.76 |
Yao et al. [7] | Single CNN | 0.86 |
Garg et al. [63] | Single CNN | 0.74 |
Barata et al. [61] | Ensemble of CNNs | 0.73 |
Our method | Single CNN | 0.88 |
Stage One Methods | BACC |
---|---|
CE | 0.82 ± 0.01 |
loss | 0.83 ± 0.00 |
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Foahom Gouabou, A.C.; Iguernaissi, R.; Damoiseaux, J.-L.; Moudafi, A.; Merad, D. End-to-End Decoupled Training: A Robust Deep Learning Method for Long-Tailed Classification of Dermoscopic Images for Skin Lesion Classification. Electronics 2022, 11, 3275. https://doi.org/10.3390/electronics11203275
Foahom Gouabou AC, Iguernaissi R, Damoiseaux J-L, Moudafi A, Merad D. End-to-End Decoupled Training: A Robust Deep Learning Method for Long-Tailed Classification of Dermoscopic Images for Skin Lesion Classification. Electronics. 2022; 11(20):3275. https://doi.org/10.3390/electronics11203275
Chicago/Turabian StyleFoahom Gouabou, Arthur Cartel, Rabah Iguernaissi, Jean-Luc Damoiseaux, Abdellatif Moudafi, and Djamal Merad. 2022. "End-to-End Decoupled Training: A Robust Deep Learning Method for Long-Tailed Classification of Dermoscopic Images for Skin Lesion Classification" Electronics 11, no. 20: 3275. https://doi.org/10.3390/electronics11203275
APA StyleFoahom Gouabou, A. C., Iguernaissi, R., Damoiseaux, J.-L., Moudafi, A., & Merad, D. (2022). End-to-End Decoupled Training: A Robust Deep Learning Method for Long-Tailed Classification of Dermoscopic Images for Skin Lesion Classification. Electronics, 11(20), 3275. https://doi.org/10.3390/electronics11203275