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学者姓名：王舒
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Diagnostic pathology, historically dependent on visual scrutiny by experts, is essential for disease detection. Advances in digital pathology and developments in computer vision technology have led to the application of artificial intelligence (AI) in this field. Despite these advancements, the variability in pathologists’ subjective interpretations of diagnostic criteria can lead to inconsistent outcomes. To meet the need for precision in cancer therapies, there is an increasing demand for accurate pathological diagnoses. Consequently, traditional diagnostic pathology is evolving towards “next-generation diagnostic pathology”, prioritizing on the development of a multi-dimensional, intelligent diagnostic approach. Using nonlinear optical effects arising from the interaction of light with biological tissues, multiphoton microscopy (MPM) enables high-resolution label-free imaging of multiple intrinsic components across various human pathological tissues. AI-empowered MPM further improves the accuracy and efficiency of diagnosis, holding promise for providing auxiliary pathology diagnostic methods based on multiphoton diagnostic criteria. In this review, we systematically outline the applications of MPM in pathological diagnosis across various human diseases, and summarize common multiphoton diagnostic features. Moreover, we examine the significant role of AI in enhancing multiphoton pathological diagnosis, including aspects such as image preprocessing, refined differential diagnosis, and the prognostication of outcomes. We also discuss the challenges and perspectives faced by the integration of MPM and AI, encompassing equipment, datasets, analytical models, and integration into the existing clinical pathways. Finally, the review explores the synergy between AI and label-free MPM to forge novel diagnostic frameworks, aiming to accelerate the adoption and implementation of intelligent multiphoton pathology systems in clinical settings. © The Author(s) 2024.
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| GB/T 7714 | Wang, S. , Pan, J. , Zhang, X. et al. Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy [J]. | Light: Science and Applications , 2024 , 13 (1) . |
| MLA | Wang, S. et al. "Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy" . | Light: Science and Applications 13 . 1 (2024) . |
| APA | Wang, S. , Pan, J. , Zhang, X. , Li, Y. , Liu, W. , Lin, R. et al. Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy . | Light: Science and Applications , 2024 , 13 (1) . |
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Abstract ：
Diagnostic pathology, historically dependent on visual scrutiny by experts, is essential for disease detection. Advances in digital pathology and developments in computer vision technology have led to the application of artificial intelligence (AI) in this field. Despite these advancements, the variability in pathologists’ subjective interpretations of diagnostic criteria can lead to inconsistent outcomes. To meet the need for precision in cancer therapies, there is an increasing demand for accurate pathological diagnoses. Consequently, traditional diagnostic pathology is evolving towards “next-generation diagnostic pathology”, prioritizing on the development of a multi-dimensional, intelligent diagnostic approach. Using nonlinear optical effects arising from the interaction of light with biological tissues, multiphoton microscopy (MPM) enables high-resolution label-free imaging of multiple intrinsic components across various human pathological tissues. AI-empowered MPM further improves the accuracy and efficiency of diagnosis, holding promise for providing auxiliary pathology diagnostic methods based on multiphoton diagnostic criteria. In this review, we systematically outline the applications of MPM in pathological diagnosis across various human diseases, and summarize common multiphoton diagnostic features. Moreover, we examine the significant role of AI in enhancing multiphoton pathological diagnosis, including aspects such as image preprocessing, refined differential diagnosis, and the prognostication of outcomes. We also discuss the challenges and perspectives faced by the integration of MPM and AI, encompassing equipment, datasets, analytical models, and integration into the existing clinical pathways. Finally, the review explores the synergy between AI and label-free MPM to forge novel diagnostic frameworks, aiming to accelerate the adoption and implementation of intelligent multiphoton pathology systems in clinical settings.
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| GB/T 7714 | Shu Wang , Junlin Pan , Xiao Zhang et al. Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy [J]. | Light:Science & Applications , 2024 , 13 (1) . |
| MLA | Shu Wang et al. "Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy" . | Light:Science & Applications 13 . 1 (2024) . |
| APA | Shu Wang , Junlin Pan , Xiao Zhang , Yueying Li , Wenxi Liu , Ruolan Lin et al. Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy . | Light:Science & Applications , 2024 , 13 (1) . |
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Abstract ：
偏振可以提高无人机的自主侦察能力，但易受到探测角度和目标材质的影响，从而降低偏振检测的鲁棒性。为此，提出一种基于偏振图像的低空伪装目标实时检测算法YOLO-P,采用融合多偏振方向信息的编码图像作为输入，应用三维卷积模块提取不同偏振方向图像之间的联系特征；引入特征增强模块对多层次特征进行进一步增强；采用跨层级特征聚合网络，充分利用不同尺度的特征信息，完成特征的有效聚合，最终联合多通道特征信息输出检测结果。构建包含10类目标的低空伪装目标偏振图像数据集PICO(Polarization Image of Camouflaged Objects)。在PICO数据集上的实验结果表明，新方法可以有效检测伪装目标，mAP_(0.5:0.95)达到52.0%,mAP_(0.5)达到91.5%,检测速率达到55.0帧/s,满足实时性要求。
Keyword ：
伪装目标检测 伪装目标检测 偏振成像 偏振成像 无人机 无人机 深度学习 深度学习 特征增强 特征增强 特征聚合 特征聚合
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| GB/T 7714 | 沈英 , 刘贤财 , 王舒 et al. 基于偏振编码图像的低空伪装目标实时检测 [J]. | 兵工学报 , 2024 , 45 (05) : 1374-1383 . |
| MLA | 沈英 et al. "基于偏振编码图像的低空伪装目标实时检测" . | 兵工学报 45 . 05 (2024) : 1374-1383 . |
| APA | 沈英 , 刘贤财 , 王舒 , 黄峰 . 基于偏振编码图像的低空伪装目标实时检测 . | 兵工学报 , 2024 , 45 (05) , 1374-1383 . |
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Ductal carcinoma in situ with microinvasion (DCISM) is a challenging subtype of breast cancer with controversial invasiveness and prognosis. Accurate diagnosis of DCISM from ductal carcinoma in situ (DCIS) is crucial for optimal treatment and improved clinical outcomes. However, there are often some suspicious small cancer nests in DCIS, and it is difficult to diagnose the presence of intact myoepithelium by conventional hematoxylin and eosin (H&E) stained images. Although a variety of biomarkers are available for immunohistochemical (IHC) staining of myoepithelial cells, no single biomarker is consistently sensitive to all tumor lesions. Here, we introduced a new diagnostic method that provides rapid and accurate diagnosis of DCISM using multiphoton microscopy (MPM). Suspicious foci in H&E-stained images were labeled as regions of interest (ROIs), and the nuclei within these ROIs were segmented using a deep learning model. MPM was used to capture images of the ROIs in H&E-stained sections. The intensity of two-photon excitation fluorescence (TPEF) in the myoepithelium was significantly different from that in tumor parenchyma and tumor stroma. Through the use of MPM, the myoepithelium and basement membrane can be easily observed via TPEF and second-harmonic generation (SHG), respectively. By fusing the nuclei in H&E-stained images with MPM images, DCISM can be differentiated from suspicious small cancer clusters in DCIS. The proposed method demonstrated good consistency with the cytokeratin 5/6 (CK5/6) myoepithelial staining method (kappa coefficient = 0.818). Accurate distinction between ductal carcinoma in situ with microinvasion (DCISM) and ductal carcinoma in situ (DCIS) is crucial for optimal treatment and improved clinical outcomes. However, current diagnostic methods are often unreliable or time-consuming. Here, the authors present a novel diagnostic method that allows rapid and accurate diagnosis of DCISM by fusing multiphoton microscopy images with H&E-stained nuclear images. Myoepithelium and basement membrane can be visualized directly on H&E-stained sections without the need for immunohistochemical staining. This approach could facilitate the clinical diagnosis of DCISM, and has the potential to optimize risk stratification and improve prognosis in DCIS patients.image
Keyword ：
basement membrane basement membrane breast cancer breast cancer ductal carcinoma in situ ductal carcinoma in situ ductal carcinoma in situ with microinvasion ductal carcinoma in situ with microinvasion image fusion image fusion multiphoton microscopy multiphoton microscopy myoepithelium myoepithelium
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| GB/T 7714 | Han, Xiahui , Liu, Yulan , Zhang, Shichao et al. Improving the diagnosis of ductal carcinoma in situ with microinvasion without immunohistochemistry: An innovative method with H&E-stained and multiphoton microscopy images [J]. | INTERNATIONAL JOURNAL OF CANCER , 2024 , 154 (10) : 1802-1813 . |
| MLA | Han, Xiahui et al. "Improving the diagnosis of ductal carcinoma in situ with microinvasion without immunohistochemistry: An innovative method with H&E-stained and multiphoton microscopy images" . | INTERNATIONAL JOURNAL OF CANCER 154 . 10 (2024) : 1802-1813 . |
| APA | Han, Xiahui , Liu, Yulan , Zhang, Shichao , Li, Lianhuang , Zheng, Liqin , Qiu, Lida et al. Improving the diagnosis of ductal carcinoma in situ with microinvasion without immunohistochemistry: An innovative method with H&E-stained and multiphoton microscopy images . | INTERNATIONAL JOURNAL OF CANCER , 2024 , 154 (10) , 1802-1813 . |
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Polarization can improve the autonomous reconnaissance capability of unmanned aerial vehicle, but it is easily interfered by the variation of detection angle and target materials, which affects the robustness of polarization detection. In this paper, a real-time low-altitude camouflaged target detection algorithm of YOLO-Polarization based on polarized images is proposed. The coded image fused with multi-polarization direction information is used as input, the 3D convolution module is applied to extract the connection features from the different polarization direction images, and a feature enhancement module (FEM) is introduced to further enhance the multi-level features. In addition, the cross-level feature aggregation network is adopted to make full use of the feature information of different scales to complete the effective aggregation of features, and finally combined with multi-channel feature information output detection results. A dataset consisting of polarized images of low-altitude camouflaged targets (PICO) which include 10 types of targets is constructed. The experimental results based on PICO dataset show that the proposed method can effectively detect the camouflaged targets, with mAP0. 5:0. 95 up to 52. 0% and mAP0. 5 up to 91. 5% . The detection rate achieves 55. 0 frames / s, which meets the requirement of real-time detection. © 2024 China Ordnance Industry Corporation. All rights reserved.
Keyword ：
Aircraft detection Aircraft detection Antennas Antennas Deep learning Deep learning Feature extraction Feature extraction Image enhancement Image enhancement Polarization Polarization Signal detection Signal detection Unmanned aerial vehicles (UAV) Unmanned aerial vehicles (UAV)
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| GB/T 7714 | Shen, Ying , Liu, Xiancai , Wang, Shu et al. Real-time Detection of Low-altitude Camouflaged Targets Based on Polarization Encoded Images [J]. | Acta Armamentarii , 2024 , 45 (5) : 1374-1383 . |
| MLA | Shen, Ying et al. "Real-time Detection of Low-altitude Camouflaged Targets Based on Polarization Encoded Images" . | Acta Armamentarii 45 . 5 (2024) : 1374-1383 . |
| APA | Shen, Ying , Liu, Xiancai , Wang, Shu , Huang, Feng . Real-time Detection of Low-altitude Camouflaged Targets Based on Polarization Encoded Images . | Acta Armamentarii , 2024 , 45 (5) , 1374-1383 . |
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Nuclei segmentation and classification play a crucial role in pathology diagnosis, enabling pathologists to analyze cellular characteristics accurately. Overlapping cluster nuclei, misdetection of small-scale nuclei, and pleomorphic nuclei-induced misclassification have always been major challenges in the nuclei segmentation and classification tasks. To this end, we introduce an auxiliary task of nuclei boundary-guided contrastive learning to enhance the representativeness and discriminative power of visual features, particularly for addressing the challenge posed by the unclear contours of adherent nuclei and small nuclei. In addition, misclassifications resulting from pleomorphic nuclei often exhibit low classification confidence, indicating a high level of uncertainty. To mitigate misclassification, we capitalize on the characteristic clustering of similar cells to propose a locality-aware class embedding module, offering a regional perspective to capture category information. Moreover, we address uncertain classification in densely aggregated nuclei by designing a top-k uncertainty attention module that leverages deep features to enhance shallow features, thereby improving the learning of contextual semantic information. We demonstrate that the proposed network outperforms the off-the-shelf methods in both nuclei segmentation and classification experiments, achieving the state-of-the-art performance. © 2024 Elsevier Ltd
Keyword ：
Classification (of information) Classification (of information) Computer aided diagnosis Computer aided diagnosis Deep learning Deep learning Image classification Image classification Semantics Semantics Semantic Segmentation Semantic Segmentation
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| GB/T 7714 | Liu, Wenxi , Zhang, Qing , Li, Qi et al. Contrastive and uncertainty-aware nuclei segmentation and classification [J]. | Computers in Biology and Medicine , 2024 , 178 . |
| MLA | Liu, Wenxi et al. "Contrastive and uncertainty-aware nuclei segmentation and classification" . | Computers in Biology and Medicine 178 (2024) . |
| APA | Liu, Wenxi , Zhang, Qing , Li, Qi , Wang, Shu . Contrastive and uncertainty-aware nuclei segmentation and classification . | Computers in Biology and Medicine , 2024 , 178 . |
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Diagnostic pathology, historically dependent on visual scrutiny by experts, is essential for disease detection. Advances in digital pathology and developments in computer vision technology have led to the application of artificial intelligence (AI) in this field. Despite these advancements, the variability in pathologists' subjective interpretations of diagnostic criteria can lead to inconsistent outcomes. To meet the need for precision in cancer therapies, there is an increasing demand for accurate pathological diagnoses. Consequently, traditional diagnostic pathology is evolving towards "next-generation diagnostic pathology", prioritizing on the development of a multi-dimensional, intelligent diagnostic approach. Using nonlinear optical effects arising from the interaction of light with biological tissues, multiphoton microscopy (MPM) enables high-resolution label-free imaging of multiple intrinsic components across various human pathological tissues. AI-empowered MPM further improves the accuracy and efficiency of diagnosis, holding promise for providing auxiliary pathology diagnostic methods based on multiphoton diagnostic criteria. In this review, we systematically outline the applications of MPM in pathological diagnosis across various human diseases, and summarize common multiphoton diagnostic features. Moreover, we examine the significant role of AI in enhancing multiphoton pathological diagnosis, including aspects such as image preprocessing, refined differential diagnosis, and the prognostication of outcomes. We also discuss the challenges and perspectives faced by the integration of MPM and AI, encompassing equipment, datasets, analytical models, and integration into the existing clinical pathways. Finally, the review explores the synergy between AI and label-free MPM to forge novel diagnostic frameworks, aiming to accelerate the adoption and implementation of intelligent multiphoton pathology systems in clinical settings. AI-empowered multiphoton microscopy enhances diagnostic accuracy and efficiency for various human diseases, evolving towards next-generation diagnostic pathology with an endogenous, multi-dimensional, and intelligent approach.
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| GB/T 7714 | Wang, Shu , Pan, Junlin , Zhang, Xiao et al. Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy [J]. | LIGHT-SCIENCE & APPLICATIONS , 2024 , 13 (1) . |
| MLA | Wang, Shu et al. "Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy" . | LIGHT-SCIENCE & APPLICATIONS 13 . 1 (2024) . |
| APA | Wang, Shu , Pan, Junlin , Zhang, Xiao , Li, Yueying , Liu, Wenxi , Lin, Ruolan et al. Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy . | LIGHT-SCIENCE & APPLICATIONS , 2024 , 13 (1) . |
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Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment.Despite advancements in optical detection capabilities through im-aging systems,including spectral,polarization,and infrared technologies,there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes.Here,this study proposes a snapshot multispectral image-based camouflaged detection model,multispectral YOLO(MS-YOLO),which utilizes the SPD-Conv and SimAM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information.Besides,the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD),which encompasses diverse scenes,target scales,and attitudes.To minimize infor-mation redundancy,MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input.Through experiments on the MSCPD,MS-YOLO achieves a mean Average Precision of 94.31％and real-time detection at 65 frames per second,which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes.Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.
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| GB/T 7714 | Shu Wang , Dawei Zeng , Yixuan Xu et al. Towards complex scenes:A deep learning-based camouflaged people detection method for snapshot multispectral images [J]. | 防务技术 , 2024 , 34 (4) : 269-281 . |
| MLA | Shu Wang et al. "Towards complex scenes:A deep learning-based camouflaged people detection method for snapshot multispectral images" . | 防务技术 34 . 4 (2024) : 269-281 . |
| APA | Shu Wang , Dawei Zeng , Yixuan Xu , Gonghan Yang , Feng Huang , Liqiong Chen . Towards complex scenes:A deep learning-based camouflaged people detection method for snapshot multispectral images . | 防务技术 , 2024 , 34 (4) , 269-281 . |
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Camouflaged target detection aims to detect targets that blend into their surroundings, but RGB has difficulty distinguishing between targets and backgrounds. While methods using multispectral image (MSI) can distinguish targets from background via spectral information, they are limited by imaging speed, resolution, and high cost for camouflaged target detection. Here, we propose a novel camouflaged target detection workflow based on reconstructed MSI from RGB image. Specifically, we propose a spectral reconstruction model, S2HFormer, which utilizes the deep neural network to fit the mapping of RGB image to MSI without additional information. And the reconstructed MSI based on S2HFormer achieves higher accuracy in both reconstruction and target detection, outperforming existing methods. Furthermore, we integrate a spectral band selection algorithm to optimize the number of bands used for improving detection efficiency. Experimental results show that the proposed method acquires MSI at 55 frames per second (FPS) and achieves an F -score of 0.925, achieving real-time (24 FPS) MSI acquisition. The evaluation indicates the effectiveness and efficiency of our method for camouflaged target detection.
Keyword ：
Camouflaged target detection Camouflaged target detection Deep learning Deep learning Multispectral Multispectral Remote Sensing Remote Sensing Spectral reconstruction Spectral reconstruction
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| GB/T 7714 | Wang, Shu , Xu, Yixuan , Zeng, Dawei et al. Deep learning-based spectral reconstruction in camouflaged target detection [J]. | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2024 , 126 . |
| MLA | Wang, Shu et al. "Deep learning-based spectral reconstruction in camouflaged target detection" . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 126 (2024) . |
| APA | Wang, Shu , Xu, Yixuan , Zeng, Dawei , Huang, Feng , Liang, Lingyu . Deep learning-based spectral reconstruction in camouflaged target detection . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2024 , 126 . |
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This paper introduces a camera-array-based super -resolution color polarization imaging system designed to simultaneously capture color and polarization information of a scene in a single shot. Existing snapshot color polarization imaging has a complex structure and limited generalizability, which are overcome by the proposed system. In addition, a novel reconstruction algorithm is designed to exploit the complementarity and correlation between the twelve channels in acquired color polarization images for simultaneous super -resolution (SR) imaging and denoising. We propose a confidence-guided SR reconstruction algorithm based on guided filtering to enhance the constraint capability of the observed data. Additionally, by introducing adaptive parameters, we effectively balance the data fidelity constraint and the regularization constraint of nonlocal sparse tensor. Simulations were conducted to compare the proposed system with a color polarization camera. The results show that color polarization images generated by the proposed system and algorithm outperform those obtained from the color polarization camera and the state -of -the -art color polarization demosaicking algorithms. Moreover, the proposed algorithm also outperforms state -of -the -art SR algorithms based on deep learning. To evaluate the applicability of the proposed imaging system and reconstruction algorithm in practice, a prototype was constructed for color polarization image acquisition. Compared with conventional acquisition, the proposed solution demonstrates a significant improvement in the reconstructed color polarization images.
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| GB/T 7714 | Huang, Feng , Chen, Yating , Wang, Xuesong et al. Joint constraints of guided filtering based confidence and nonlocal sparse tensor for color polarization super-resolution imaging [J]. | OPTICS EXPRESS , 2024 , 32 (2) : 2364-2391 . |
| MLA | Huang, Feng et al. "Joint constraints of guided filtering based confidence and nonlocal sparse tensor for color polarization super-resolution imaging" . | OPTICS EXPRESS 32 . 2 (2024) : 2364-2391 . |
| APA | Huang, Feng , Chen, Yating , Wang, Xuesong , Wang, Shu , Wu, Xianyu . Joint constraints of guided filtering based confidence and nonlocal sparse tensor for color polarization super-resolution imaging . | OPTICS EXPRESS , 2024 , 32 (2) , 2364-2391 . |
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