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Purpose: Prostate cancer primarily arises from the glandular epithelium. Histomophometric techniques have been used to assess the glandular epithelium in automated detection and classification pipelines; however, they are often rigid in their implementation, and their performance suffers on large datasets where variation in staining, imaging, and preparation is difficult to control. The purpose of this study is to quantify performance of a pixelwise segmentation algorithm that was trained using different combinations of weak and strong stroma, epithelium, and lumen labels in a prostate histology dataset. Approach: We have combined weakly labeled datasets generated using simple morphometric techniques and high-quality labeled datasets from human observers in prostate biopsy cores to train a convolutional neural network for use in whole mount prostate labeling pipelines. With trained networks, we characterize pixelwise segmentation of stromal, epithelium, and lumen (SEL) regions on both biopsy core and whole-mount H&E-stained tissue. Results: We provide evidence that by simply training a deep learning algorithm on weakly labeled data generated from rigid morphometric methods, we can improve the robustness of classification over the morphometric methods used to train the classifier. Conclusions: We show that not only does our approach of combining weak and strong labels for training the CNN improve qualitative SEL labeling within tissue but also the deep learning generated labels are superior for cancer classification in a higher-order algorithm over the morphometrically derived labels it was trained on., (© The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.)

The onset and development of preschoolers' awareness of sex role stereotypes, gender labeling, gender identity, and sex-typed toy preference were explored in 26-, 31-, and 36-month-old children. Gender labeling, gender identity, sex-typed toy preferences, and awareness of adult sex role differences were observed in significantly more 26-month-old children than would have been expected by chance. Verbal gender labeling was observed in a majority of 26-month-olds, while verbal and nonverbal gender identity were observed in a majority of 31-month-olds. Nonverbal gender labeling and awareness of adult sex role differences were observed in a majority of children by 36 months. No evidence of awareness of sex differences in children's toys was found in the majority of children at any age. Awareness of sex role differences in children's toys was not related to awareness of adult sex role differences. Brighter children were more aware of adult sex role differences. Sex-typed toy preference was not related to awareness of sex role differences but was related to gender identity. Predictors of sex role development included the mothers' employment, and the father's personality traits, attitudes toward women, and sex-typed activities in the home. Implications for theories of early sex role development are discussed.