歴史記述を通して見たクメール・タイの相互認識の比較研究: イデオロギーと言説

早大学位記番号:新6554早稲田大

« Il tète, mais il n’y a pas de lait ». Hiérarchisation et hybridation des savoirs sur le colostrum au Cambodge

International audienc

« Il tète, mais il n’y a pas de lait ». Hiérarchisation et hybridation des savoirs sur le colostrum au Cambodge

International audienc

Women’s request and other factors associated with caesarean sections in Phnom Penh, Cambodia

International audienceC-sections are an increasingly performed medical practice which can save lives but may also lead to major complications. Through a mixed methods study conducted in 2015 in Cambodia, we aimed to analyze the reasons for requesting a c-section and to explore factors that are associated with c-sections. 60% of the women in the cohort who gave birth by c-section reported having requested it. Through 31 in-depth interviews, we determined the reasons given by women for requesting a c-section before and during labour. Before labour, reasons for requesting a c-section were: choosing the delivery date; bringing luck and joy to the family; protecting the genitals, and the belief that c-section is safer for the mother and for the baby. Reasons given during labour were fear, pain, and having no more energy. We also observed two major factors driving the women's request for a c-section: family support for requesting a c-section, and the over-usage of ultrasound examinations. Our multivariate analysis of the interviews of 143 women before and after delivery showed that having a previous c-section, delivering in a private facility, being older than median at the time of sexual debut, residing outside of Phnom Penh and having the delivery costs covered by the family were all factors independently and significantly associated with a higher chance of c-section delivery. We conclude that women are not well informed to give consent for c-delivery, and that their request is often affected by false belief and poor knowledge

Semantic Segmentation of Pancreatic Cancer in Endoscopic Ultrasound Images Using Deep Learning Approach

Endoscopic ultrasonography (EUS) plays an important role in diagnosing pancreatic cancer. Surgical therapy is critical to pancreatic cancer survival and can be planned properly, with the characteristics of the target cancer determined. The physical characteristics of the pancreatic cancer, such as size, location, and shape, can be determined by semantic segmentation of EUS images. This study proposes a deep learning approach for the segmentation of pancreatic cancer in EUS images. EUS images were acquired from 150 patients diagnosed with pancreatic cancer. A network with deep attention features (DAF-Net) is proposed for pancreatic cancer segmentation using EUS images. The performance of the deep learning models (U-Net, Attention U-Net, and DAF-Net) was evaluated by 5-fold cross-validation. For the evaluation metrics, the Dice similarity coefficient (DSC), intersection over union (IoU), receiver operating characteristic (ROC) curve, and area under the curve (AUC) were chosen. Statistical analysis was performed for different stages and locations of the cancer. DAF-Net demonstrated superior segmentation performance for the DSC, IoU, AUC, sensitivity, specificity, and precision with scores of 82.8%, 72.3%, 92.7%, 89.0%, 98.1%, and 85.1%, respectively. The proposed deep learning approach can provide accurate segmentation of pancreatic cancer in EUS images and can effectively assist in the planning of surgical therapies