Breast tumor grading network based on adaptive fusion and microscopic imaging

Pan Huang; Peng He; Xing Yang; Jiayang Luo; Hualiang Xiao; Sukun Tian; Peng Feng

doi:10.12086/oee.2023.220158

Opto-Electronic Engineering, Volume. 50, Issue 1, 220158(2023)

Breast tumor grading network based on adaptive fusion and microscopic imaging

Pan Huang1... Peng He1, Xing Yang2, Jiayang Luo1, Hualiang Xiao3,*, Sukun Tian4,** and Peng Feng1,*** |Show fewer author(s)

Author Affiliations

¹Key Laboratory of Optoelectronic Technology & Systems (Ministry of Education), College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China

²College of Computer and Network Security, Chengdu University of Technology, Chengdu, Sichuan 610000, China

³Daping Hospital, Department of Pathology, Army Military Medical University, Chongqing 400037, China

⁴School of Mechanical Engineering, Shandong University, Jinan, Shandong 250000, China

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Figures & Tables(11)

Fig. 1. Block diagram of the algorithm in this paper. (a) ER IHC staining based microscopic imaging procedure for breast cancer pathology; (b) AMFNet

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Fig. 2. Detail diagram of the AFF method implementation

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Fig. 3. AFRM method implementation schematic

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Fig. 4. Visually interpretative results comparisons with SOTA and our method on the breast cancer IHC microscopic imaging

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Fig. 5. Comparison of visualization results of histopathological images of brain cancer

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Table 1. Distribution of the number of ER IHC datasets for breast cancer
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Table 1. Distribution of the number of ER IHC datasets for breast cancer
Datasets Parameter
Grade I Grade II Grade III Image size Total
Training set 268 355 360 224×224 983
Validation set 90 118 120 224×224 328
Testing set 90 119 120 224×224 329
Total 448 592 600 224×224 1640

Table 2. Classification confusion matrix
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Table 2. Classification confusion matrix
Reality Forecast result
Positive Negative
Positive True positive (TP) False negative (FN)
Negative False positive (FP) True negative (TN)

Table 3. Ablation of AMF method in ER IHC pathological microimaging of breast cancer

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Table 3. Ablation of AMF method in ER IHC pathological microimaging of breast cancer

Model	AMF method			Average acc/%	P	R	F1	AUC
Model	MOO	AFRM	AFF	Average acc/%	P	R	F1	AUC
AMFNet (ViT-AMCNN blocks)	✗	✗	✓	69.00	0.6934	0.6900	0.6903	0.7643
	✓	✗	✗	92.40	0.9240	0.9240	0.9240	0.9423
	✓	✓	✗	93.92	0.9403	0.9392	0.9394	0.9532
	✓	✓	✓	95.14	0.9520	0.9514	0.9513	0.9617

Table 4. Tumor grading accuracy of breast cancer ER IHC histopathology microscopic imaging

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Table 4. Tumor grading accuracy of breast cancer ER IHC histopathology microscopic imaging

Model	Grade I acc/%	Grade II acc/%	Grade III acc/%	Average acc/%	P	R	F1	AUC
Inception V3^[36]	74.71	78.19	78.01	77.20	0.7721	0.7720	0.7717	0.8290
Xception V3^[37]	71.82	68.40	76.42	72.34	0.7226	0.7234	0.7226	0.7925
ResNet50^[38]	72.96	73.77	76.08	74.47	0.7524	0.7447	0.7439	0.8085
DenseNet121^[39]	82.80	77.53	81.63	80.55	0.8059	0.8055	0.8047	0.8541
DenseNet121+Nonlocal^[40]	85.88	84.39	81.97	83.89	0.8396	0.8389	0.8391	0.8791
DenseNet121+SENet^[41]	79.55	83.27	84.39	82.67	0.8272	0.8267	0.8266	0.8700
DenseNet121+CBAM^[42]	82.76	81.20	84.80	82.98	0.8307	0.8298	0.8294	0.8723
DenseNet121+HIENet^[43]	86.21	85.59	85.48	85.71	0.8585	0.8571	0.8572	0.8928
FABNet^[15]	86.05	91.29	84.90	87.54	0.8765	0.8754	0.8752	0.9036
ViT-S/16^[27]	54.02	55.87	69.20	60.18	60.37	60.18	0.6023	0.6970
ViT-B/16^[27]	85.87	86.32	84.17	85.41	0.8546	0.8541	0.8541	0.8913
ViT-B/32^[27]	68.60	70.00	73.98	71.12	0.7114	0.7112	0.7107	0.7799
ViT-L/16^[27]	78.98	79.17	81.23	79.94	0.8113	0.7994	0.7987	0.8430
ViT-L/32^[27]	50.35	61.21	59.83	58.36	0.6018	0.5836	0.5774	0.6770
AMFNet (ours)	92.66	97.02	95.12	95.14 $↑$ 7.6	0.9520	0.9514	0.9513	0.9617

Table 5. Distribution of the number of brain cancer histopathology image datasets
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Table 5. Distribution of the number of brain cancer histopathology image datasets
Datasets Grade I Grade II Grade III Grade IV Total
Training set 276 492 873 891 2532
Validation set 91 164 290 296 841
Testing set 91 164 290 296 841
Total 458 820 1453 1483 4214

Table 6. Comparison table of tumor grading accuracy of histopathological images of brain cancer

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Table 6. Comparison table of tumor grading accuracy of histopathological images of brain cancer

Model	Metrics
Model	Grade I acc/%	Grade II acc/%	Grade III acc/%	Grade IV acc/%	Average acc/%	P	R	F1	AUC
Inception V3^[36]	84.16	68.11	80.62	78.05	77.76	0.7779	0.7776	0.7766	0.8575
Xception V3^[37]	84.95	72.34	78.31	80.80	78.83	0.7976	0.7883	0.7874	0.8588
ResNet50^[38]	82.00	65.62	66.43	71.12	69.80	0.6963	0.6980	0.6961	0.8091
DenseNet121^[39]	87.05	74.75	74.86	81.75	78.95	0.7978	0.7895	0.7858	0.8625
DenseNet121+Nonlocal^[40]	91.84	81.90	86.70	89.39	87.40	0.8763	0.8740	0.8727	0.9195
DenseNet121+SENet^[41]	96.22	84.97	88.05	90.25	89.18	0.8925	0.8918	0.8911	0.9279
DenseNet121+CBAM^[42]	92.71	82.00	89.04	86.77	87.40	0.8750	0.8740	0.8726	0.9162
DenseNet121+HIENet^[43]	95.70	85.99	87.81	87.50	88.23	0.8851	0.8823	0.8820	0.9244
FABNet^[15]	93.68	87.70	90.97	90.82	90.61	0.9072	0.9061	0.9057	0.9391
ViT-S/16^[27]	65.98	46.98	65.97	69.73	64.21	0.6375	0.6421	0.6359	0.7489
ViT-B/16^[27]	83.90	80.00	87.83	86.83	85.61	0.8618	0.8561	0.8553	0.9028
ViT-B/32^[27]	81.48	62.59	78.93	77.20	75.74	0.7550	0.7574	0.7541	0.8319
ViT-L/16^[27]	70.79	54.49	72.05	73.56	69.32	0.6897	0.6932	0.6902	0.7808
ViT-L/32^[27]	75.49	58.02	73.76	75.08	71.70	0.7152	0.7170	0.7134	0.8084
AMFNet (ours)	98.32	93.38	94.30	93.90	94.41 $↑$ 3.8	0.9451	0.9441	0.9442	0.9611

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Pan Huang, Peng He, Xing Yang, Jiayang Luo, Hualiang Xiao, Sukun Tian, Peng Feng. Breast tumor grading network based on adaptive fusion and microscopic imaging[J]. Opto-Electronic Engineering, 2023, 50(1): 220158

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Paper Information

Category: Article

Received: Jul. 8, 2022

Accepted: Sep. 6, 2022

Published Online: Feb. 27, 2023

The Author Email:

DOI:10.12086/oee.2023.220158

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Distribution of the number of ER IHC datasets for breast cancer

Table 1. Distribution of the number of ER IHC datasets for breast cancer

Table 2. Classification confusion matrix

Table 2. Classification confusion matrix

Table 3. Ablation of AMF method in ER IHC pathological microimaging of breast cancer

Table 3. Ablation of AMF method in ER IHC pathological microimaging of breast cancer

Table 4. Tumor grading accuracy of breast cancer ER IHC histopathology microscopic imaging

Table 4. Tumor grading accuracy of breast cancer ER IHC histopathology microscopic imaging

Table 5. Distribution of the number of brain cancer histopathology image datasets

Table 5. Distribution of the number of brain cancer histopathology image datasets

Table 6. Comparison table of tumor grading accuracy of histopathological images of brain cancer

Table 6. Comparison table of tumor grading accuracy of histopathological images of brain cancer

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