始于1996年的信息行为(Information Seeking In Context,ISIC)国际会议每两年举办一次,其关注的主题是"基于情景的信息需求"。2014年9月,第十届国际信息行为会议在英国利兹大学举行,本文对此次会议收录的50篇论文进行分析,将...始于1996年的信息行为(Information Seeking In Context,ISIC)国际会议每两年举办一次,其关注的主题是"基于情景的信息需求"。2014年9月,第十届国际信息行为会议在英国利兹大学举行,本文对此次会议收录的50篇论文进行分析,将论文主题分为社交媒体环境下的信息行为研究、非社交媒体环境下的信息行为研究和认知科学角度下的信息行为研究三大方面,并对其进行归纳、总结和分析,以展现社交媒体环境下的信息行为研究进展。展开更多
Skin cancer has been recognized as one of the most lethal and complex types of cancer for over a decade.The diagnosis of skin cancer is of paramount importance,yet the process is intricate and challenging.The analysis...Skin cancer has been recognized as one of the most lethal and complex types of cancer for over a decade.The diagnosis of skin cancer is of paramount importance,yet the process is intricate and challenging.The analysis and modeling of human skin pose significant difficulties due to its asymmetrical nature,the visibility of dense hair,and the presence of various substitute characteristics.The texture of the epidermis is notably different from that of normal skin,and these differences are often evident in cases of unhealthy skin.As a consequence,the development of an effective method for monitoring skin cancer has seen little progress.Moreover,the task of diagnosing skin cancer from dermoscopic images is particularly challenging.It is crucial to diagnose skin cancer at an early stage,despite the high cost associated with the procedure,as it is an expensive process.Unfortunately,the advancement of diagnostic techniques for skin cancer has been limited.To address this issue,there is a need for a more accurate and efficient method for identifying and categorizing skin cancer cases.This involves the evaluation of specific characteristics to distinguish between benign and malignant skin cancer occurrences.We present and evaluate several techniques for segmentation,categorized into three main types:thresholding,edge-based,and region-based.These techniques are applied to a dataset of 200 benign and melanoma lesions from the Hospital Pedro Hispano(PH2)collection.The evaluation is based on twelve distinct metrics,which are designed to measure various types of errors with particular clinical significance.Additionally,we assess the effectiveness of these techniques independently for three different types of lesions:melanocytic nevi,atypical nevi,and melanomas.The first technique is capable of classifying lesions into two categories:atypical nevi and melanoma,achieving the highest accuracy score of 90.00%with the Otsu(3-level)method.The second technique also classifies lesions into two categories:common nevi and melanoma,achieving a score of 90.80%with the Binarized Sauvola method.展开更多
文摘始于1996年的信息行为(Information Seeking In Context,ISIC)国际会议每两年举办一次,其关注的主题是"基于情景的信息需求"。2014年9月,第十届国际信息行为会议在英国利兹大学举行,本文对此次会议收录的50篇论文进行分析,将论文主题分为社交媒体环境下的信息行为研究、非社交媒体环境下的信息行为研究和认知科学角度下的信息行为研究三大方面,并对其进行归纳、总结和分析,以展现社交媒体环境下的信息行为研究进展。
文摘Skin cancer has been recognized as one of the most lethal and complex types of cancer for over a decade.The diagnosis of skin cancer is of paramount importance,yet the process is intricate and challenging.The analysis and modeling of human skin pose significant difficulties due to its asymmetrical nature,the visibility of dense hair,and the presence of various substitute characteristics.The texture of the epidermis is notably different from that of normal skin,and these differences are often evident in cases of unhealthy skin.As a consequence,the development of an effective method for monitoring skin cancer has seen little progress.Moreover,the task of diagnosing skin cancer from dermoscopic images is particularly challenging.It is crucial to diagnose skin cancer at an early stage,despite the high cost associated with the procedure,as it is an expensive process.Unfortunately,the advancement of diagnostic techniques for skin cancer has been limited.To address this issue,there is a need for a more accurate and efficient method for identifying and categorizing skin cancer cases.This involves the evaluation of specific characteristics to distinguish between benign and malignant skin cancer occurrences.We present and evaluate several techniques for segmentation,categorized into three main types:thresholding,edge-based,and region-based.These techniques are applied to a dataset of 200 benign and melanoma lesions from the Hospital Pedro Hispano(PH2)collection.The evaluation is based on twelve distinct metrics,which are designed to measure various types of errors with particular clinical significance.Additionally,we assess the effectiveness of these techniques independently for three different types of lesions:melanocytic nevi,atypical nevi,and melanomas.The first technique is capable of classifying lesions into two categories:atypical nevi and melanoma,achieving the highest accuracy score of 90.00%with the Otsu(3-level)method.The second technique also classifies lesions into two categories:common nevi and melanoma,achieving a score of 90.80%with the Binarized Sauvola method.
