The Lantern, 2020-2021

One Thousand and One is Never Enough • House on Hazel Ave. • Crooked Men at Crooked Alley • Home • Honeybee • The Witch\u27s Daughter • Traveling to Reyu • December 31st, 2019 • The Dominator Rolls the Dice Again • Red Flowers • Military Ball • Drowning in Color • Early Bird • Introspection • Hot Water • Reaching Into Space • Floating Marigolds Before COVID-19 • Smokestack 4 • Longing • His Fifth Year on Amstel Road • Wonderful Moments • Clean Glass • Betty, the Debutante • Teakettles Have it Easy • Fuimos, Somos y Seramos Parte de la Historia de la Isla • Kitchen Table • She Couldn\u27t • Cooling Down • Not so Precious Stones • Domestic Wild • Violet Eater • I Will be Sweet • Flavor of Life • Clogged Artery • All Twenty-Six • The Greatest • From Ashes of War to Golden Cities • A Good Thing • Introduction • Devotion • Life of the Gambler • Impressions: Or a Dining Table\u27s Soliloquy • Looking Glass • Montgomery Pie • Under the Hill • Paperback Lesbian • Girl With Pearl Earring • Your Mirror • Jacket • Illusions • Strawberry Girl (Raw Sugar, Shattered Glass) • I Don\u27t Jam With Instagram • The Morning After Saturday • A Brisk Monday Morning • Emergence • Politeness and Pattern Recognition • Douglas Adams\u27 Guide to Florida • A Love Story With Femininity • Roots • Dysmorphia IIIhttps://digitalcommons.ursinus.edu/lantern/1189/thumbnail.jp

CSA forum:(Re)defining fashion

Originally presented as a panel at the Costume Society of America 2011 Symposium, this essay advocates incorporation of a new paradigm in fashion scholarship, teaching, and learning that acknowledges fashion\u27s global and diverse dimensions and occurrences. A brief historiography describes the underpinnings of the dominant perspective used in theorizing fashion. The rationale for the new perspective includes examination of the previous view\u27s inherent limitations - that fashion originated in fourteenthcentury Europe and that fashion is Western - which have affected the entire conceptualization of dress history. Examples of continually changing styles for women\u27s hair in Imperial Rome and in Chinese lip coloring demonstrate temporal and spatial contexts of fashion outside of the Western capitalist arena. A general definition of fashion is presented. The essay concludes with discussion of contemporary world dress and world fashion, review of recent research, and strategies for the academy in scholarship and teaching of fashion and dress history.© Costume Society of America 2012

Superior skin cancer classification by the combination of human and artificial intelligence

Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification). Findings: Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5% Interpretation: Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems. (C) 2019 The Author(s). Published by Elsevier Ltd

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

Background: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. Methods: Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. Findings: Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). Interpretation: Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001). (C) 2019 The Author(s). Published by Elsevier Ltd