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Promises and limitations of deep learning for medical image segmentation 深度学习在医学图像分割中的应用前景与局限性
Journal of medical artificial intelligence Pub Date : 2019-01-01 DOI: 10.21037/JMAI.2019.01.01
C. Perone, J. Cohen-Adad
{"title":"Promises and limitations of deep learning for medical image segmentation","authors":"C. Perone, J. Cohen-Adad","doi":"10.21037/JMAI.2019.01.01","DOIUrl":"https://doi.org/10.21037/JMAI.2019.01.01","url":null,"abstract":"It is not a secret that recent advances in deep learning (1) methods have achieved a scientific and engineering milestone in many different fields such as natural language processing, computer vision, speech recognition, object detection, and segmentation, to name a few. Different applications of deep learning to medical imaging started to appear first in workshops, conferences and then in journals. According to a recent survey (2), the number of papers grew rapidly in 2015 and 2016. Nowadays, deep learning methods are pervasive throughout the entire medical imaging community, with Convolutional Neural Networks (CNNs) being the most used model for tasks such as dense prediction (or segmentation), detection and classification. In the same survey, which analyzed more than 300 contributions in the field, the authors found that computed tomography (CT) was the third most used imaging modality.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.21037/JMAI.2019.01.01","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46836557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 32
Psychogenic non-epileptic seizures, recent advances and commentary on, Vasta et al., the application of artificial intelligence to understand the biological bases of the disorder 心因性非癫痫性发作，最近的进展和评论，Vasta等人，应用人工智能来理解这种疾病的生物学基础
Journal of medical artificial intelligence Pub Date : 2018-12-01 DOI: 10.21037/JMAI.2018.12.01
N. Boutros
{"title":"Psychogenic non-epileptic seizures, recent advances and commentary on, Vasta et al., the application of artificial intelligence to understand the biological bases of the disorder","authors":"N. Boutros","doi":"10.21037/JMAI.2018.12.01","DOIUrl":"https://doi.org/10.21037/JMAI.2018.12.01","url":null,"abstract":"As many as 33 per 100,000 people experience episodes of paroxysmal impairment associated with a range of manifestations that can be motor, sensory, and/or mental and closely mimic and frequently mistaken for epileptic seizures (1). These episodes are termed psychogenic non-epileptic seizures (PNES). The prevalence of PNES episodes is much higher in epilepsy practices, reaching as high as 30% (2). The diagnosis of PNES remains a process of excluding epilepsy and thus leads to an average time from onset of these paroxysms to diagnosis of close to seven years.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.21037/JMAI.2018.12.01","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43175427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Computer-aided diagnosis to differentiate colorectal polyps: are we nearing primetime? 计算机辅助诊断鉴别结直肠息肉：我们接近黄金时段了吗？
Journal of medical artificial intelligence Pub Date : 2018-11-01 DOI: 10.21037/JMAI.2018.11.02
N. Shahidi, M. Byrne
{"title":"Computer-aided diagnosis to differentiate colorectal polyps: are we nearing primetime?","authors":"N. Shahidi, M. Byrne","doi":"10.21037/JMAI.2018.11.02","DOIUrl":"https://doi.org/10.21037/JMAI.2018.11.02","url":null,"abstract":"Colorectal cancer screening has proven to be an effective preventative health measure (1). This is, in part, achieved by the identification and removal of neoplastic adenomatous polyps. However, within the rectum and sigmoid colon, non-neoplastic hyperplastic polyps are also common.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.21037/JMAI.2018.11.02","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46169198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Can texture features computed from the joint intensity distribution of different MRI sequences accurately predict prostate cancer grade? 根据不同MRI序列的关节强度分布计算的纹理特征能否准确预测前列腺癌症分级？
Journal of medical artificial intelligence Pub Date : 2018-05-11 DOI: 10.21037/JMAI.2018.11.01
V. Stavrinides, L. C. Echeverria, H. Whitaker
{"title":"Can texture features computed from the joint intensity distribution of different MRI sequences accurately predict prostate cancer grade?","authors":"V. Stavrinides, L. C. Echeverria, H. Whitaker","doi":"10.21037/JMAI.2018.11.01","DOIUrl":"https://doi.org/10.21037/JMAI.2018.11.01","url":null,"abstract":"The diagnostic landscape of prostate cancer has evolved rapidly, from prostate-specific antigen (PSA) testing to exciting new technologies that allow visualization of the disease, moving away from random sampling to targeted biopsies. Multiparametric magnetic resonance imaging (mpMRI) is a new modality that combines T2-weighted (T2W), diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) sequences, each designed to reveal specific microstructural features typically associated with malignancy such as increased vascularity and cellularity.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41440471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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