本文的研究方法是以认知语言学与体貌学的主要理论为基础,以建构对于“事件语义学”及“体貌学”的一种新的研究模式。 本文研究主张人类的认知活动具有普遍性:任何文化、民族或语系的人,对不同“事件”的想像能力是有普遍性的,但不同语言能够通过特定的语法标记来凸显一个事件的不同方面,而有的语言只能通过迂回形式或其他手段去表达同样的概念。凡是涉及某种结果的事件都蕴涵不同的“事件相”(situational phases),这几个事件阶段有时对应于特定的语法标记:在现代汉语中的趋向补语、结果补语、时体标记等,都可以凸显某个事件过程的特定阶段,也就充当特定的“事件相标记”。 本文尝试将“体貌标记”、“事件相标记”以及“无标记的事件相”分开讨论,以证明“言语事件”(discoursive situation)与“形象事件”(visualized situation)的独立性。“言语事件”等同于说话者通过语言形式能够表达出来的言语信息,而“形象事件”等同于该语言形式所引起的认知场景。前者表示特定的“信息性”功能(informative)而后者相当于一种“想像性”(immaginative)活动。 本文从跨语言的角度,尽可能科学地证明语言表达形式不对应于与其相关的认知场景,并且在历时的平面上相信“形象事件”的“焦点移动”(Focussing Shift)此认知现象,是导致时体标记语法化的因素之一。 Osten Dahl,Bybee,Perkins Pagliuca(1994)等研究者曾经证明“完整体”的语法化演变在所有的自然语言中一般经过“结果体”、“完成体”与“完整体”三个基本阶段;最早的“结果体标记”虚化至“完成体标记”此语法化现象也导致认知上的观察方式由“内部视点”转成“外部视点”。 我们认为,最早的“结果体”或“完结体”基本上等同于一个“事件相”:在很多自然语言发展的历时过程中,参照视点沿着一种逆向的观察方式:结果状态→现时状态→行为本身。在印欧语言中,完整性标记(Perfective Markers)的语法化过程显示跨语言性的认知规律:语法化程度越高,参照视点越靠近原来行为的动态实现,反而语法化程度越低,参照视点离原来行为的动态实现越远。 “焦点移动”的历时演变正好符合参照视点逐渐向原来行为靠近的认知机制;我们认为在语法化的过程中,“相”是“体”的“父母”,就是它的来源,但在共时的平面上“体貌”和“事件相”显示一定的差异,且现代汉语、土耳其语等语言的“事件相系统”能作为典型的例子。 本文在“体貌”及“事件相”两个平行而相异的概念上,通过“焦点移动论”特别解释“结果体标记”的历时演变及其类型学的共时蕴涵。

结果体与结果相的跨语言研究:a comparative approach

A reported 96,480 people were diagnosed with melanoma in the United States in 2019, leading to 7230 reported deaths. Early-stage identification of suspicious pigmented lesions (SPLs) in primary care settings can lead to improved melanoma prognosis and a possible 20-fold reduction in treatment cost. Despite this clinical and economic value, efficient tools for SPL detection are mostly absent. To bridge this gap, we developed an SPL analysis system for wide-field images using deep convolutional neural networks (DCNNs) and applied it to a 38,283 dermatological dataset collected from 133 patients and publicly available images. These images were obtained from a variety of consumer-grade cameras (15,244 nondermoscopy) and classified by three board-certified dermatologists. Our system achieved more than 90.3% sensitivity (95% confidence interval, 90 to 90.6) and 89.9% specificity (89.6 to 90.2%) in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds, avoiding the need for cumbersome individual lesion imaging. We also present a new method to extract intrapatient lesion saliency (ugly duckling criteria) on the basis of DCNN features from detected lesions. This saliency ranking was validated against three board-certified dermatologists using a set of 135 individual wide-field images from 68 dermatological patients not included in the DCNN training set, exhibiting 82.96% (67.88 to 88.26%) agreement with at least one of the top three lesions in the dermatological consensus ranking. This method could allow for rapid and accurate assessments of pigmented lesion suspiciousness within a primary care visit and could enable improved patient triaging, utilization of resources, and earlier treatment of melanoma.

Using deep learning for dermatologist-level detection of suspicious pigmented skin lesions from wide-field images.

How does the accuracy of deep neural network models trained to classify clinical images of skin conditions vary across skin color? While recent studies demonstrate computer vision models can serve as a useful decision support tool in healthcare and provide dermatologist-level classification on a number of specific tasks, darker skin is under-represented in the data. Most publicly available data sets do not include Fitzpatrick skin type labels. We annotate 16,577 clinical images sourced from two dermatology atlases with Fitzpatrick skin type labels and open-source these annotations. Based on these labels, we find that there are significantly more images of light skin types than dark skin types in this dataset. We train a deep neural network model to classify 114 skin conditions and find that the model is most accurate on skin types similar to those it was trained on. In addition, we evaluate how an algorithmic approach to identifying skin tones, individual typology angle, compares with Fitzpatrick skin type labels annotated by a team of human labelers.

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Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset

Characteristics of publicly available skin cancer image datasets: a systematic review

/pdf/exaid-a-multimodal-explanation-framework-for-computer-aided-1xz3ygy6.pdf

Judith S. Birkenfeld

Papers

Using deep learning for dermatologist-level detection of suspicious pigmented skin lesions from wide-field images.

Computer-aided classification of suspicious pigmented lesions using wide-field images.