Learning from dermoscopic images in association with clinical metadata for skin lesion segmentation and classification.

Automatic segmentation and classification of lesions are two clinically significant tasks in the computer-aided diagnosis of skin diseases. Both tasks are challenging due to the nonnegligible lesion differences in dermoscopic images from different patients. In this paper, we propose a novel pipeline to efficiently implement skin lesions' segmentation and classification tasks, which consists of a segmentation network and a classification network. To improve the performance of the segmentation network, we propose a novel module of Multi-Scale Holistic Feature Exploration (MSH) to thoroughly exploit perceptual clues latent among multi-scale feature maps as synthesized by the decoder. The MSH module enables holistic exploration of features across multiple scales to more effectively support downstream image analysis tasks. To boost the performance of the classification network, we propose a novel module of Cross-Modality Collaborative Feature Exploration (CMC) to discover latent discriminative features by collaboratively exploiting potential relationships between cross-modal features of dermoscopic images and clinical metadata. The CMC module enables dynamically capturing versatile interaction effects among cross-modal features during the model's representation learning procedure by discriminatively and adaptively learning the interaction weight associated with each crossmodality feature pair. In addition, to effectively reduce background noise and boost the lesion discrimination ability of the classification network, we crop the images based on lesion masks generated by the best segmentation model. We evaluate the proposed pipeline on the four public skin lesion datasets, where the ISIC 2018 and PH2 are for segmentation, and the ISIC 2019 and ISIC 2020 are combined into a new dataset, ISIC 2019&2020, for classification. It achieves a Jaccard index of 83.31% and 90.14% in skin lesion segmentation, an AUC of 97.98% and an Accuracy of 92.63% in skin lesion classification, which is superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Last but not least, the new model for segmentation utilizes much fewer model parameters (3.3 M) than its peer approaches, leading to a greatly reduced number of labeled samples required for model training, which obtains substantially stronger robustness than its peers.

Icariin protects mouse Leydig cell testosterone synthesis from the adverse effects of di(2-ethylhexyl) phthalate.

Infertility caused by environmental pollution is becoming a global problem, but an effective prevention or treatment is lacking. Icariin (ICA) is a flavonoid used in traditional Chinese medicine. The present study investigated the possible roles of ICA in preventing testicular dysfunction caused by di(2-ethylhexyl) phthalate (DEHP), one of the most studied environmental endocrine disruptors. Cultured mouse Leydig cells were pretreated with ICA and exposed to DEHP to determine ICA effects upon cell proliferation, reactive oxygen species (ROS) levels, mitochondrial membrane potential (Δψm), testosterone levels and the expression of transcription factor SF-1 and steroidogenic enzymes (CYP11, 3ß-HSD and 17ß-HSD), which play critical roles in androgen production. Our results showed that ICA reversed the adverse effect of DEHP on Leydig cell proliferation, and decreased ROS levels and elevated Δψm levels. Also, ICA promoted testosterone production and up-regulated the expression of SF-1 and steroidogenic enzymes. We investigated ICA actions in vivo, using male mice administrated DEHP followed by ICA. Exposure to DEHP decreased epididymal sperm counts and disrupted seminiferous tubules, and both of these effects were reversed by ICA treatment. These results showed that the mechanisms of ICA in protecting mouse testes against DEHP-induced damage involves the prevention of ROS accumulation and promotion of testosterone secretion.