Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery

Thanks to recent advances in CNNs, solid improvements have been made in semantic segmentation of high resolution remote sensing imagery. However, most of the previous works have not fully taken into account the specific difficulties that exist in remote sensing tasks. One of such difficulties is that objects are small and crowded in remote sensing imagery. To tackle with this challenging task we have proposed a novel architecture called local feature extraction (LFE) module attached on top of dilated front-end module. The LFE module is based on our findings that aggressively increasing dilation factors fails to aggregate local features due to sparsity of the kernel, and detrimental to small objects. The proposed LFE module solves this problem by aggregating local features with decreasing dilation factor. We tested our network on three remote sensing datasets and acquired remarkably good results for all datasets especially for small objects

Protocol for a multicentre, prospective, cohort study to investigate patient satisfaction and quality of life after immediate breast reconstruction in Japan: the SAQLA study

Introduction The aim of breast reconstruction (BR) is to improve patients' health-related quality of life (HRQOL). Therefore, measuring patient-reported outcomes (PROs) would clarify the value and impact of BR on a patient's life and thus would provide evidence-based information to help decision-making. The Satisfaction and Quality of Life After Immediate Breast Reconstruction study aimed to investigate satisfaction and HRQOL in Japanese patients with breast cancer who undergo immediate breast reconstruction (IBR). Methods and analysis This ongoing prospective, observational multicentre study will assess 406 patients who had unilateral breast cancer and underwent mastectomy and IBR, and were recruited from April 2018 to July 2019. All participants were recruited from seven hospitals: Okayama University Hospital, Iwate Medical University Hospital, The Cancer Institute Hospital of Japanese Foundation for Cancer Research, Showa University Hospital, University of Tsukuba Hospital, Osaka University Hospital and Yokohama City University Medical Center. The patients will be followed up for 36 months postoperatively. The primary endpoint of this study will be the time-dependent changes in BREAST-Q satisfaction with breast subscale scores for 12 months after reconstructive surgery, which will be collected via an electronic PRO system. Ethics and dissemination This study will be performed in accordance with the Ethical Guidelines for Medical and Health Research Involving Human Subjects published by Japan's Ministry of Education, Science and Technology and the Ministry of Health, Labour and Welfare, the modified Act on the Protection of Personal Information and the Declaration of Helsinki. This study protocol was approved by the institutional ethics committee at the Okayama University Graduate School of Medicine, Dentistry, on 2 February 2018 (1801-039) and all other participating sites. The findings of this trial will be submitted to an international peer-reviewed journal

Soft Sensor Modeling for Identifying Significant Process Variables with Time Delays

Soft sensors have been widely employed in industrial processes to estimate process variables that are difficult to measure in real time. Taking into account process dynamics in soft sensor models is important not only for high predictability, but also for the interpretation of the models. One simple way of introducing dynamics is to use process variables X with time-delays. In this study, we propose a strategy of building soft sensor models for selecting appropriate variables X with time-delays, by incorporating ensemble learning into genetic algorithm-based process variables and dynamics selection (GAVDS). The construction of soft sensor models using simulation data sets and publicly available process operation data sets showed that the ensemble GAVDS method was superior to GAVDS in terms of model predictability. Furthermore, it appeared to select reasonable time delay regions for process variables X