BME frontiers最新文献_第8页

Three-Dimensional Shear Wave Elastography Using a 2D Row Column Addressing (RCA) Array. 使用2D行-列寻址（RCA）阵列的三维剪切波弹性成像。

BME frontiers Pub Date : 2022-07-04 eCollection Date: 2022-01-01 DOI: 10.34133/2022/9879632

Zhijie Dong, Jihun Kim, Chengwu Huang, Matthew R Lowerison, U-Wai Lok, Shigao Chen, Pengfei Song

{"title":"Three-Dimensional Shear Wave Elastography Using a 2D Row Column Addressing (RCA) Array.","authors":"Zhijie Dong, Jihun Kim, Chengwu Huang, Matthew R Lowerison, U-Wai Lok, Shigao Chen, Pengfei Song","doi":"10.34133/2022/9879632","DOIUrl":"10.34133/2022/9879632","url":null,"abstract":"Objective. To develop a 3D shear wave elastography (SWE) technique using a 2D row column addressing (RCA) array, with either external vibration or acoustic radiation force (ARF) as the shear wave source. Impact Statement. The proposed method paves the way for clinical translation of 3D SWE based on the 2D RCA, providing a low-cost and high volume rate solution that is compatible with existing clinical systems. Introduction. SWE is an established ultrasound imaging modality that provides a direct and quantitative assessment of tissue stiffness, which is significant for a wide range of clinical applications including cancer and liver fibrosis. SWE requires high frame rate imaging for robust shear wave tracking. Due to the technical challenges associated with high volume rate imaging in 3D, current SWE techniques are typically confined to 2D. Advancing SWE from 2D to 3D is significant because of the heterogeneous nature of tissue, which demands 3D imaging for accurate and comprehensive evaluation. Methods. A 3D SWE method using a RCA array was developed with a volume rate up to 2000 Hz. The performance of the proposed method was systematically evaluated on tissue-mimicking elasticity phantoms and in an in vivo case study. Results. 3D shear wave motion induced by either external vibration or ARF was successfully detected with the proposed method. Robust 3D shear wave speed maps were reconstructed for phantoms and in vivo. Conclusion. The high volume rate 3D imaging provided by the 2D RCA array provides a robust and practical solution for 3D SWE with a clear pathway for future clinical translation.","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9879632"},"PeriodicalIF":0.0,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Virtual Staining, Segmentation, and Classification of Blood Smears for Label-Free Hematology Analysis. 用于无标记血液学分析的血液涂片的虚拟染色、分割和分类。

IF 5

BME frontiers Pub Date : 2022-07-01 eCollection Date: 2022-01-01 DOI: 10.34133/2022/9853606

Nischita Kaza, Ashkan Ojaghi, Francisco E Robles

{"title":"Virtual Staining, Segmentation, and Classification of Blood Smears for Label-Free Hematology Analysis.","authors":"Nischita Kaza, Ashkan Ojaghi, Francisco E Robles","doi":"10.34133/2022/9853606","DOIUrl":"10.34133/2022/9853606","url":null,"abstract":"Objective and Impact Statement. We present a fully automated hematological analysis framework based on single-channel (single-wavelength), label-free deep-ultraviolet (UV) microscopy that serves as a fast, cost-effective alternative to conventional hematology analyzers. Introduction. Hematological analysis is essential for the diagnosis and monitoring of several diseases but requires complex systems operated by trained personnel, costly chemical reagents, and lengthy protocols. Label-free techniques eliminate the need for staining or additional preprocessing and can lead to faster analysis and a simpler workflow. In this work, we leverage the unique capabilities of deep-UV microscopy as a label-free, molecular imaging technique to develop a deep learning-based pipeline that enables virtual staining, segmentation, classification, and counting of white blood cells (WBCs) in single-channel images of peripheral blood smears. Methods. We train independent deep networks to virtually stain and segment grayscale images of smears. The segmented images are then used to train a classifier to yield a quantitative five-part WBC differential. Results. Our virtual staining scheme accurately recapitulates the appearance of cells under conventional Giemsa staining, the gold standard in hematology. The trained cellular and nuclear segmentation networks achieve high accuracy, and the classifier can achieve a quantitative five-part differential on unseen test data. Conclusion. This proposed automated hematology analysis framework could greatly simplify and improve current complete blood count and blood smear analysis and lead to the development of a simple, fast, and low-cost, point-of-care hematology analyzer.","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9853606"},"PeriodicalIF":5.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Endoscopic Coregistered Ultrasound Imaging and Precision Histotripsy: Initial In Vivo Evaluation. 内窥镜配准超声成像和精确组织切片术：初步体内评估。

BME frontiers Pub Date : 2022-07-01 eCollection Date: 2022-01-01 DOI: 10.34133/2022/9794321

Thomas G Landry, Jessica Gannon, Eli Vlaisavljevich, Matthew G Mallay, Jeffrey K Woodacre, Sidney Croul, James P Fawcett, Jeremy A Brown

{"title":"Endoscopic Coregistered Ultrasound Imaging and Precision Histotripsy: Initial In Vivo Evaluation.","authors":"Thomas G Landry, Jessica Gannon, Eli Vlaisavljevich, Matthew G Mallay, Jeffrey K Woodacre, Sidney Croul, James P Fawcett, Jeremy A Brown","doi":"10.34133/2022/9794321","DOIUrl":"10.34133/2022/9794321","url":null,"abstract":"Objective. Initial performance evaluation of a system for simultaneous high-resolution ultrasound imaging and focused mechanical submillimeter histotripsy ablation in rat brains. Impact Statement. This study used a novel combination of high-resolution imaging and histotripsy in an endoscopic form. This would provide neurosurgeons with unprecedented accuracy in targeting and executing nonthermal ablations in minimally invasive surgeries. Introduction. Histotripsy is a safe and effective nonthermal focused ablation technique. However, neurosurgical applications, such as brain tumor ablation, are difficult due to the presence of the skull. Current devices are too large to use in the minimally invasive approaches surgeons prefer. We have developed a combined imaging and histotripsy endoscope to provide neurosurgeons with a new tool for this application. Methods. The histotripsy component had a 10 mm diameter, operating at 6.3 MHz. Affixed within a cutout hole in its center was a 30 MHz ultrasound imaging array. This coregistered pair was used to ablate brain tissue of anesthetized rats while imaging. Histological sections were examined, and qualitative descriptions of ablations and basic shape descriptive statistics were generated. Results. Complete ablations with submillimeter area were produced in seconds, including with a moving device. Ablation progress could be monitored in real time using power Doppler imaging, and B-mode was effective for monitoring post-ablation bleeding. Collateral damage was minimal, with a 100 μm maximum distance of cellular damage from the ablation margin. Conclusion. The results demonstrate a promising hardware suite to enable precision ablations in endoscopic procedures or fundamental preclinical research in histotripsy, neuroscience, and cancer.","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9794321"},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Impedance Imaging of Cells and Tissues: Design and Applications. 细胞和组织的阻抗成像：设计和应用。

BME frontiers Pub Date : 2022-06-09 DOI: 10.34133/2022/9857485

Raziyeh Bounik, Fernando Cardes, Hasan Ulusan, Mario M Modena, Andreas Hierlemann

{"title":"Impedance Imaging of Cells and Tissues: Design and Applications.","authors":"Raziyeh Bounik, Fernando Cardes, Hasan Ulusan, Mario M Modena, Andreas Hierlemann","doi":"10.34133/2022/9857485","DOIUrl":"10.34133/2022/9857485","url":null,"abstract":"Due to their label-free and noninvasive nature, impedance measurements have attracted increasing interest in biological research. Advances in microfabrication and integrated-circuit technology have opened a route to using large-scale microelectrode arrays for real-time, high-spatiotemporal-resolution impedance measurements of biological samples. In this review, we discuss different methods and applications of measuring impedance for cell and tissue analysis with a focus on impedance imaging with microelectrode arrays in in vitro applications. We first introduce how electrode configurations and the frequency range of the impedance analysis determine the information that can be extracted. We then delve into relevant circuit topologies that can be used to implement impedance measurements and their characteristic features, such as resolution and data-acquisition time. Afterwards, we detail design considerations for the implementation of new impedance-imaging devices. We conclude by discussing future fields of application of impedance imaging in biomedical research, in particular applications where optical imaging is not possible, such as monitoring of ex vivo tissue slices or microelectrode-based brain implants.","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"1-21"},"PeriodicalIF":0.0,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7612906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10620728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

A Transparent Ultrasound Array for Real-Time Optical, Ultrasound, and Photoacoustic Imaging. 用于实时光学、超声和光声成像的透明超声阵列。

BME frontiers Pub Date : 2022-06-08 eCollection Date: 2022-01-01 DOI: 10.34133/2022/9871098

Haoyang Chen, Sumit Agrawal, Mohamed Osman, Josiah Minotto, Shubham Mirg, Jinyun Liu, Ajay Dangi, Quyen Tran, Thomas Jackson, Sri-Rajasekhar Kothapalli

{"title":"A Transparent Ultrasound Array for Real-Time Optical, Ultrasound, and Photoacoustic Imaging.","authors":"Haoyang Chen, Sumit Agrawal, Mohamed Osman, Josiah Minotto, Shubham Mirg, Jinyun Liu, Ajay Dangi, Quyen Tran, Thomas Jackson, Sri-Rajasekhar Kothapalli","doi":"10.34133/2022/9871098","DOIUrl":"10.34133/2022/9871098","url":null,"abstract":"Objective and Impact Statement. Simultaneous imaging of ultrasound and optical contrasts can help map structural, functional, and molecular biomarkers inside living subjects with high spatial resolution. There is a need to develop a platform to facilitate this multimodal imaging capability to improve diagnostic sensitivity and specificity. Introduction. Currently, combining ultrasound, photoacoustic, and optical imaging modalities is challenging because conventional ultrasound transducer arrays are optically opaque. As a result, complex geometries are used to coalign both optical and ultrasound waves in the same field of view. Methods. One elegant solution is to make the ultrasound transducer transparent to light. Here, we demonstrate a novel transparent ultrasound transducer (TUT) linear array fabricated using a transparent lithium niobate piezoelectric material for real-time multimodal imaging. Results. The TUT-array consists of 64 elements and centered at ~6 MHz frequency. We demonstrate a quad-mode ultrasound, Doppler ultrasound, photoacoustic, and fluorescence imaging in real-time using the TUT-array directly coupled to the tissue mimicking phantoms. Conclusion. The TUT-array successfully showed a multimodal imaging capability and has potential applications in diagnosing cancer, neurological, and vascular diseases, including image-guided endoscopy and wearable imaging.","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9871098"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521654/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of Moderate Intensity Focused Ultrasound (MIFU) for Ocular Drug Delivery. 用于眼部给药的中等强度聚焦超声（MIFU）的发展。

BME frontiers Pub Date : 2022-06-08 eCollection Date: 2022-01-01 DOI: 10.34133/2022/9840678

Alejandra Gonzalez-Calle, Runze Li, Isaac Asante, Juan Carlos Martinez-Camarillo, Stan Louie, Qifa Zhou, Mark S Humayun

{"title":"Development of Moderate Intensity Focused Ultrasound (MIFU) for Ocular Drug Delivery.","authors":"Alejandra Gonzalez-Calle, Runze Li, Isaac Asante, Juan Carlos Martinez-Camarillo, Stan Louie, Qifa Zhou, Mark S Humayun","doi":"10.34133/2022/9840678","DOIUrl":"10.34133/2022/9840678","url":null,"abstract":"The purpose of this study is to develop a method for delivering antiinflammatory agents of high molecular weight (e.g., Avastin) into the posterior segment that does not require injections into the eye (i.e., intravitreal injections; IVT). Diseases affecting the posterior segment of the eye are currently treated with monthly to bimonthly intravitreal injections, which can predispose patients to severe albeit rare complications like endophthalmitis, retinal detachment, traumatic cataract, and/or increased intraocular. In this study, we show that one time moderate intensity focused ultrasound (MIFU) treatment can facilitate the penetration of large molecules across the scleral barrier, showing promising evidence that this is a viable method to deliver high molecular weight medications not invasively. To validate the efficacy of the drug delivery system, IVT injections of vascular endothelial growth factor (VEGF) were used to create an animal model of retinopathy. The creation of this model allowed us to test anti-VEGF medications and evaluate the efficacy of the treatment. In vivo testing showed that animals treated with our MIFU device improved on the retinal tortuosity and clinical dilation compared to the control group while evaluating fluorescein angiogram (FA) Images.","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9840678"},"PeriodicalIF":0.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Learning to Localize Cross-Anatomy Landmarks in X-Ray Images with a Universal Model. 学习用通用模型定位X射线图像中的交叉解剖标志。

IF 5

BME frontiers Pub Date : 2022-06-08 eCollection Date: 2022-01-01 DOI: 10.34133/2022/9765095

Heqin Zhu, Qingsong Yao, Li Xiao, S Kevin Zhou

{"title":"Learning to Localize Cross-Anatomy Landmarks in X-Ray Images with a Universal Model.","authors":"Heqin Zhu, Qingsong Yao, Li Xiao, S Kevin Zhou","doi":"10.34133/2022/9765095","DOIUrl":"10.34133/2022/9765095","url":null,"abstract":"Objective and Impact Statement. In this work, we develop a universal anatomical landmark detection model which learns once from multiple datasets corresponding to different anatomical regions. Compared with the conventional model trained on a single dataset, this universal model not only is more light weighted and easier to train but also improves the accuracy of the anatomical landmark location. Introduction. The accurate and automatic localization of anatomical landmarks plays an essential role in medical image analysis. However, recent deep learning-based methods only utilize limited data from a single dataset. It is promising and desirable to build a model learned from different regions which harnesses the power of big data. Methods. Our model consists of a local network and a global network, which capture local features and global features, respectively. The local network is a fully convolutional network built up with depth-wise separable convolutions, and the global network uses dilated convolution to enlarge the receptive field to model global dependencies. Results. We evaluate our model on four 2D X-ray image datasets totaling 1710 images and 72 landmarks in four anatomical regions. Extensive experimental results show that our model improves the detection accuracy compared to the state-of-the-art methods. Conclusion. Our model makes the first attempt to train a single network on multiple datasets for landmark detection. Experimental results qualitatively and quantitatively show that our proposed model performs better than other models trained on multiple datasets and even better than models trained on a single dataset separately.","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9765095"},"PeriodicalIF":5.0,"publicationDate":"2022-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521670/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA) for Adipose Tissue Segmentation. 用于脂肪组织分割的联合优化空间直方图UNET架构（JOSHUA）。

BME frontiers Pub Date : 2022-06-03 eCollection Date: 2022-01-01 DOI: 10.34133/2022/9854084

Joshua K Peeples, Julie F Jameson, Nisha M Kotta, Jonathan M Grasman, Whitney L Stoppel, Alina Zare

{"title":"Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA) for Adipose Tissue Segmentation.","authors":"Joshua K Peeples, Julie F Jameson, Nisha M Kotta, Jonathan M Grasman, Whitney L Stoppel, Alina Zare","doi":"10.34133/2022/9854084","DOIUrl":"10.34133/2022/9854084","url":null,"abstract":"Objective. We aim to develop a machine learning algorithm to quantify adipose tissue deposition at surgical sites as a function of biomaterial implantation. Impact Statement. To our knowledge, this study is the first investigation to apply convolutional neural network (CNN) models to identify and segment adipose tissue in histological images from silk fibroin biomaterial implants. Introduction. When designing biomaterials for the treatment of various soft tissue injuries and diseases, one must consider the extent of adipose tissue deposition. In this work, we analyzed adipose tissue accumulation in histological images of sectioned silk fibroin-based biomaterials excised from rodents following subcutaneous implantation for 1, 2, 4, or 8 weeks. Current strategies for quantifying adipose tissue after biomaterial implantation are often tedious and prone to human bias during analysis. Methods. We used CNN models with novel spatial histogram layer(s) that can more accurately identify and segment regions of adipose tissue in hematoxylin and eosin (H&E) and Masson's trichrome stained images, allowing for determination of the optimal biomaterial formulation. We compared the method, Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA), to the baseline UNET model and an extension of the baseline model, attention UNET, as well as to versions of the models with a supplemental attention-inspired mechanism (JOSHUA+ and UNET+). Results. The inclusion of histogram layer(s) in our models shows improved performance through qualitative and quantitative evaluation. Conclusion. Our results demonstrate that the proposed methods, JOSHUA and JOSHUA+, are highly beneficial for adipose tissue identification and localization. The new histological dataset and code used in our experiments are publicly available.","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9854084"},"PeriodicalIF":0.0,"publicationDate":"2022-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recent Advancements in Ultrasound Transducer: From Material Strategies to Biomedical Applications. 超声换能器的最新进展：从材料策略到生物医学应用。

BME frontiers Pub Date : 2022-05-11 eCollection Date: 2022-01-01 DOI: 10.34133/2022/9764501

Jiapu Li, Yuqing Ma, Tao Zhang, K Kirk Shung, Benpeng Zhu

引用次数: 23

A Low-Cost High-Performance Data Augmentation for Deep Learning-Based Skin Lesion Classification. 用于基于深度学习的皮肤损伤分类的低成本高性能数据增强。

IF 5

BME frontiers Pub Date : 2022-04-26 eCollection Date: 2022-01-01 DOI: 10.34133/2022/9765307

Shuwei Shen, Mengjuan Xu, Fan Zhang, Pengfei Shao, Honghong Liu, Liang Xu, Chi Zhang, Peng Liu, Peng Yao, Ronald X Xu

{"title":"A Low-Cost High-Performance Data Augmentation for Deep Learning-Based Skin Lesion Classification.","authors":"Shuwei Shen, Mengjuan Xu, Fan Zhang, Pengfei Shao, Honghong Liu, Liang Xu, Chi Zhang, Peng Liu, Peng Yao, Ronald X Xu","doi":"10.34133/2022/9765307","DOIUrl":"10.34133/2022/9765307","url":null,"abstract":"Objective and Impact Statement. There is a need to develop high-performance and low-cost data augmentation strategies for intelligent skin cancer screening devices that can be deployed in rural or underdeveloped communities. The proposed strategy can not only improve the classification performance of skin lesions but also highlight the potential regions of interest for clinicians' attention. This strategy can also be implemented in a broad range of clinical disciplines for early screening and automatic diagnosis of many other diseases in low resource settings. Methods. We propose a high-performance data augmentation strategy of search space 101, which can be combined with any model through a plug-and-play mode and search for the best argumentation method for a medical database with low resource cost. Results. With EfficientNets as a baseline, the best BACC of HAM10000 is 0.853, outperforming the other published models of \"single-model and no-external-database\" for ISIC 2018 Lesion Diagnosis Challenge (Task 3). The best average AUC performance on ISIC 2017 achieves 0.909 (±0.015), exceeding most of the ensembling models and those using external datasets. Performance on Derm7pt archives the best BACC of 0.735 (±0.018) ahead of all other related studies. Moreover, the model-based heatmaps generated by Grad-CAM++ verify the accurate selection of lesion features in model judgment, further proving the scientific rationality of model-based diagnosis. Conclusion. The proposed data augmentation strategy greatly reduces the computational cost for clinically intelligent diagnosis of skin lesions. It may also facilitate further research in low-cost, portable, and AI-based mobile devices for skin cancer screening and therapeutic guidance.","PeriodicalId":72430,"journal":{"name":"BME frontiers","volume":"2022 ","pages":"9765307"},"PeriodicalIF":5.0,"publicationDate":"2022-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10521644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41241362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0