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Chen Y, Dong Y, Si L, Yang W, Du S, Tian X, Li C, Liao Q, Ma H. Dual Polarization Modality Fusion Network for Assisting Pathological Diagnosis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:304-316. [PMID: 36155433 DOI: 10.1109/tmi.2022.3210113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Polarization imaging is sensitive to sub-wavelength microstructures of various cancer tissues, providing abundant optical characteristics and microstructure information of complex pathological specimens. However, how to reasonably utilize polarization information to strengthen pathological diagnosis ability remains a challenging issue. In order to take full advantage of pathological image information and polarization features of samples, we propose a dual polarization modality fusion network (DPMFNet), which consists of a multi-stream CNN structure and a switched attention fusion module for complementarily aggregating the features from different modality images. Our proposed switched attention mechanism could obtain the joint feature embeddings by switching the attention map of different modality images to improve their semantic relatedness. By including a dual-polarization contrastive training scheme, our method can synthesize and align the interaction and representation of two polarization features. Experimental evaluations on three cancer datasets show the superiority of our method in assisting pathological diagnosis, especially in small datasets and low imaging resolution cases. Grad-CAM visualizes the important regions of the pathological images and the polarization images, indicating that the two modalities play different roles and allow us to give insightful corresponding explanations and analysis on cancer diagnosis conducted by the DPMFNet. This technique has potential to facilitate the performance of pathological aided diagnosis and broaden the current digital pathology boundary based on pathological image features.
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Pham TTH, Nguyen HP, Luu TN, Le NB, Vo VT, Huynh NT, Phan QH, Le TH. Combined Mueller matrix imaging and artificial intelligence classification framework for Hepatitis B detection. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:075002. [PMID: 36451700 PMCID: PMC9321198 DOI: 10.1117/1.jbo.27.7.075002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 07/15/2022] [Indexed: 06/02/2023]
Abstract
SIGNIFICANCE The combination of polarized imaging with artificial intelligence (AI) technology has provided a powerful tool for performing an objective and precise diagnosis in medicine. AIM An approach is proposed for the detection of hepatitis B (HB) virus using a combined Mueller matrix imaging technique and deep learning method. APPROACH In the proposed approach, Mueller matrix imaging polarimetry is applied to obtain 4 × 4 Mueller matrix images of 138 HBsAg-containing (positive) serum samples and 136 HBsAg-free (negative) serum samples. The kernel estimation density results show that, of the 16 Mueller matrix elements, elements M 22 and M 33 provide the best discriminatory power between the positive and negative samples. RESULTS As a result, M 22 and M 33 are taken as the inputs to five different deep learning models: Xception, VGG16, VGG19, ResNet 50, and ResNet150. It is shown that the optimal classification accuracy (94.5%) is obtained using the VGG19 model with element M 22 as the input. CONCLUSIONS Overall, the results confirm that the proposed hybrid Mueller matrix imaging and AI framework provides a simple and effective approach for HB virus detection.
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Affiliation(s)
- Thi-Thu-Hien Pham
- International University, School of Biomedical Engineering, HCMC, Ho Chi Minh City, Vietnam
- Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Hoang-Phuoc Nguyen
- International University, School of Biomedical Engineering, HCMC, Ho Chi Minh City, Vietnam
- Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Thanh-Ngan Luu
- International University, School of Biomedical Engineering, HCMC, Ho Chi Minh City, Vietnam
- Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Ngoc-Bich Le
- International University, School of Biomedical Engineering, HCMC, Ho Chi Minh City, Vietnam
- Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Van-Toi Vo
- International University, School of Biomedical Engineering, HCMC, Ho Chi Minh City, Vietnam
- Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam
| | - Ngoc-Trinh Huynh
- University of Medicine and Pharmacy, Department of Pharmacognosy, HCMC, Ho Chi Minh City, Vietnam
| | - Quoc-Hung Phan
- National United University, Department of Mechanical Engineering, Miaoli, Taiwan
| | - Thanh-Hai Le
- Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam
- Ho Chi Minh City University of Technology (HCMUT), Department of Mechatronics, Ho Chi Minh City, Vietnam
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Wan J, Dong Y, Xue JH, Lin L, Du S, Dong J, Yao Y, Li C, Ma H. Polarization-based probabilistic discriminative model for quantitative characterization of cancer cells. BIOMEDICAL OPTICS EXPRESS 2022; 13:3339-3354. [PMID: 35781945 PMCID: PMC9208602 DOI: 10.1364/boe.456649] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/05/2022] [Accepted: 04/26/2022] [Indexed: 05/25/2023]
Abstract
We propose a polarization-based probabilistic discriminative model for deriving a set of new sigmoid-transformed polarimetry feature parameters, which not only enables accurate and quantitative characterization of cancer cells at pixel level, but also accomplish the task with a simple and stable model. By taking advantages of polarization imaging techniques, these parameters enable a low-magnification and wide-field imaging system to separate the types of cells into more specific categories that previously were distinctive under high magnification. Instead of blindly choosing the model, the L0 regularization method is used to obtain the simplified and stable polarimetry feature parameter. We demonstrate the model viability by using the pathological tissues of breast cancer and liver cancer, in each of which there are two derived parameters that can characterize the cells and cancer cells respectively with satisfactory accuracy and sensitivity. The stability of the final model opens the possibility for physical interpretation and analysis. This technique may bypass the typically labor-intensive and subjective tumor evaluating system, and could be used as a blueprint for an objective and automated procedure for cancer cell screening.
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Affiliation(s)
- Jiachen Wan
- Guangdong Engineering Center of
Polarization Imaging and Sensing Technology, Tsinghua Shenzhen
International Graduate School, Tsinghua
University, Shenzhen 518055, China
- Equal contributors
| | - Yang Dong
- Guangdong Engineering Center of
Polarization Imaging and Sensing Technology, Tsinghua Shenzhen
International Graduate School, Tsinghua
University, Shenzhen 518055, China
- Center for Precision Medicine and
Healthcare, Tsinghua-Berkeley Shenzhen Institute,
Tsinghua University, Shenzhen 518071,
China
- Equal contributors
| | - Jing-Hao Xue
- Department of Statistical Science,
University College London, London WC1E 6BT,
UK
| | - Liyan Lin
- Department of Pathology,
Fujian Medical University Cancer Hospital,
Fujian Cancer Hospital, Fuzhou 350014, China
| | - Shan Du
- Department of Pathology,
University of Chinese Academy of Sciences Shenzhen
Hospital, Shenzhen 518106, China
| | - Jia Dong
- Guangdong Engineering Center of
Polarization Imaging and Sensing Technology, Tsinghua Shenzhen
International Graduate School, Tsinghua
University, Shenzhen 518055, China
| | - Yue Yao
- Guangdong Engineering Center of
Polarization Imaging and Sensing Technology, Tsinghua Shenzhen
International Graduate School, Tsinghua
University, Shenzhen 518055, China
- Center for Precision Medicine and
Healthcare, Tsinghua-Berkeley Shenzhen Institute,
Tsinghua University, Shenzhen 518071,
China
| | - Chao Li
- Department of Pathology,
Fujian Medical University Cancer Hospital,
Fujian Cancer Hospital, Fuzhou 350014, China
| | - Hui Ma
- Guangdong Engineering Center of
Polarization Imaging and Sensing Technology, Tsinghua Shenzhen
International Graduate School, Tsinghua
University, Shenzhen 518055, China
- Center for Precision Medicine and
Healthcare, Tsinghua-Berkeley Shenzhen Institute,
Tsinghua University, Shenzhen 518071,
China
- Department of Physics,
Tsinghua University, Beijing 100084,
China
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