An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue
Abstract
:Simple Summary
Abstract
1. Introduction
- The first stage of an AI-based system for multiclass grading of OSCC which can potentially improve objectivity and reproducibility of histopathological examination, as well as reduce the time necessary for pathological inspections.
- The second stage of an AI-based system for segmentation of tumor on epithelial and stromal regions which can assist the clinician in discovering new informative features. It has great potential in the quantification of qualitative clinic-pathological features in order to predict tumor invasion and metastasis.
- A new preprocessing methodology based on the stationary wavelet transform (SWT) is proposed to enhance high-frequency components in the case of multiclass classification and to extract low-level features in the case of semantic segmentation. This approach allows more effective predictions and improves the robustness of the entire AI-based system.
Related Work
2. Materials and Methods
2.1. Dataset Description
2.2. Preprocessing Method Based on Stationary Wavelet Transform and Mapping Function
- no decimation step—provides redundant information,
- better time-frequency localization, and
- translation-invariance.
2.3. AI-Based Models
2.3.1. Xception
2.3.2. ResNet50 and −101
2.3.3. MobileNetv2
2.4. DeepLabv3+
2.5. Evaluation Criteria
3. Results
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Characteristic of the Patients | % | |
---|---|---|
Sex | F | 35 |
M | 65 | |
Age | To 49 | 6 |
50–59 | 13 | |
60–69 | 58 | |
+70 | 23 | |
Smoking | Y | 69 |
N | 31 | |
Lymph Node Metastases | Y | 46 |
N | 54 | |
Histological Grade (G) | I | 50 |
II | 33 | |
III | 17 |
Hyperparameter | Possible Parameters |
---|---|
a | 0–0.1 |
b | 0–0.1 |
c | 0–0.1 |
d | 0.001–1 |
Wavelet function | Haar, sym2, db2, bior1.3 |
Layer | Output | Layers | ResNet50 | ResNet101 |
---|---|---|---|---|
Number of Repeating Layers | ||||
Conv1 | 112 × 112 | 7 × 7, 64, stride 2 | ×1 | ×1 |
3 × 3 max pool, stride 2 | ×1 | ×1 | ||
Conv2_x | 56 × 56 | 1 × 1, 64 | ×3 | ×3 |
3 × 3, 64 | ||||
1 × 1, 256 | ||||
Conv3_x | 28 × 28 | 1 × 1, 128 | ×4 | ×4 |
3 × 3, 128 | ||||
1 × 1, 512 | ||||
Conv4_x | 14 × 14 | 1 × 1, 256 | ×6 | ×23 |
3 × 3, 256 | ||||
1 × 1, 1024 | ||||
Conv5_x | 7 × 7 | 1 × 1, 512 | ×3 | ×3 |
3 × 3, 512 | ||||
1 × 1, 2048 | ||||
1 × 1 | Flatten | ×1 | ×1 | |
3-d Fully Connected | ||||
Softmax |
Input | Operator | Expansion Factor (t) | Number of Output Channels (c) | Repeating Number (n) | Stride (s) |
---|---|---|---|---|---|
224 × 224 × 3 | conv2d | - | 32 | 1 | 2 |
112 × 112 × 32 | bottleneck | 1 | 16 | 1 | 1 |
112 × 112 × 16 | bottleneck | 6 | 24 | 2 | 2 |
56 × 56 × 24 | bottleneck | 6 | 32 | 3 | 2 |
28 × 28 × 32 | bottleneck | 6 | 64 | 4 | 2 |
14 × 14 × 64 | bottleneck | 6 | 96 | 3 | 1 |
14 × 14 × 96 | bottleneck | 6 | 160 | 3 | 2 |
7 × 7 × 160 | bottleneck | 6 | 320 | 1 | 1 |
7 × 7 × 320 | conv2d 1 × 1 | - | 1280 | 1 | 1 |
7 × 7 × 1280 | avgpool 7 × 7 | - | - | 1 | - |
1 × 1 × 1280 | fully connected (Softmax) | - | 3 | - |
Algorithm | AUCmacro ± σ | AUCmicro ± σ |
---|---|---|
ResNet50 | 0.871 ± 0.105 | 0.864 ± 0.090 |
ResNet101 | 0.882 ± 0.125 | 0.890 ± 0.112 |
Xception | 0.929 ± 0.087 | 0.942 ± 0.074 |
MobileNetv2 | 0.877 ± 0.062 | 0.900 ± 0.049 |
Parameters | Xception + SWT | |||||
---|---|---|---|---|---|---|
a | b | c | d | Wavelet | AUCmacro ± σ | AUCmicro ± σ |
0.0084 | 0.0713 | 0.0599 | 0.0566 | sym2 | 0.956 ± 0.054 | 0.964 ± 0.040 |
0.0091 | 0.0301 | 0.0086 | 0.3444 | db2 | 0.963 ± 0.042 | 0.966 ± 0.027 |
0.0063 | 0.0021 | 0.0771 | 0.3007 | db2 | 0.947 ± 0.092 | 0.954 ± 0.069 |
0.0081 | 0.0933 | 0.0469 | 0.2520 | haar | 0.952 ± 0.056 | 0.958 ± 0.050 |
0.0053 | 0.0575 | 0.0649 | 0.1694 | bior1.3 | 0.962 ± 0.050 | 0.965 ± 0.046 |
mIOU ± σ | F1 ± σ | Accuracy ± σ | Precision ± σ | Sensitivity ± σ | Specificity ± σ | ||
---|---|---|---|---|---|---|---|
DeepLabv3+ & Xception_65 | Original | 0.864 ± 0.020 | 0.933 ± 0.058 | 0.934 ± 0.012 | 0.933 ± 0.019 | 0.967 ± 0.013 | 0.873 ± 0.017 |
sym2 | 0.874 ± 0.037 | 0.953 ± 0.016 | 0.939 ± 0.019 | 0.950 ± 0.025 | 0.956 ± 0.012 | 0.908 ± 0.040 | |
db2 | 0.876 ± 0.032 | 0.953 ± 0.016 | 0.940 ± 0.017 | 0.952 ± 0.019 | 0.955 ± 0.014 | 0.911 ± 0.031 | |
Haar | 0.879 ± 0.027 | 0.955 ± 0.014 | 0.941 ± 0.015 | 0.951 ± 0.018 | 0.958 ± 0.016 | 0.910 ± 0.026 | |
bior1.3 | 0.874 ± 0.030 | 0.953 ± 0.015 | 0.939 ± 0.016 | 0.948 ± 0.020 | 0.958 ± 0.021 | 0.904 ± 0.027 |
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Musulin, J.; Štifanić, D.; Zulijani, A.; Ćabov, T.; Dekanić, A.; Car, Z. An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue. Cancers 2021, 13, 1784. https://doi.org/10.3390/cancers13081784
Musulin J, Štifanić D, Zulijani A, Ćabov T, Dekanić A, Car Z. An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue. Cancers. 2021; 13(8):1784. https://doi.org/10.3390/cancers13081784
Chicago/Turabian StyleMusulin, Jelena, Daniel Štifanić, Ana Zulijani, Tomislav Ćabov, Andrea Dekanić, and Zlatan Car. 2021. "An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue" Cancers 13, no. 8: 1784. https://doi.org/10.3390/cancers13081784
APA StyleMusulin, J., Štifanić, D., Zulijani, A., Ćabov, T., Dekanić, A., & Car, Z. (2021). An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue. Cancers, 13(8), 1784. https://doi.org/10.3390/cancers13081784