Bi-directional Dermoscopic Feature Learning and Multi-scale Consistent Decision Fusion for Skin Lesion Segmentation

Accurate segmentation of skin lesion from dermoscopic images is a crucial part of computer-aided diagnosis of melanoma. It is challenging due to the fact that dermoscopic images from different patients have non-negligible lesion variation, which causes difficulties in anatomical structure learning and consistent skin lesion delineation. In this paper, we propose a novel bi-directional dermoscopic feature learning (biDFL) framework to model the complex correlation between skin lesions and their informative context. By controlling feature information passing through two complementary directions, a substantially rich and discriminative feature representation is achieved. Specifically, we place biDFL module on the top of a CNN network to enhance high-level parsing performance. Furthermore, we propose a multi-scale consistent decision fusion (mCDF) that is capable of selectively focusing on the informative decisions generated from multiple classification layers. By analysis of the consistency of the decision at each position, mCDF automatically adjusts the reliability of decisions and thus allows a more insightful skin lesion delineation. The comprehensive experimental results show the effectiveness of the proposed method on skin lesion segmentation, achieving state-of-the-art performance consistently on two publicly available dermoscopic image databases.Comment: Accepted to TI

Simulation of High-Altitude Meteorological Data Used to Environment Impact Assessment by MM5 Model

AbstractThe high-altitude meteorological data on the 27km resolution, with 149×149 grids in the whole country, are generated by application of mesoscale numerical model MM5. The raw data used by the model include the United States USGS data, including terrain, land use, the composition of the vegetation data, and so on. Original meteorological data are the reanalysis data of the US National Centers for Environmental Prediction of the NCEP/NCAR. According to the need of environment impact assessment (EIA), the high-altitude meteorological data contain 21 layers below 550 hPa height. The data mainly include atmospheric pressure, altitude, dry bulb temperature, dew point temperature, wind direction, wind speed, relative humidity. High-altitude meteorological data generated in this study, can be directly applied to the EIA prediction model and serve for EIA

Technology Review System of Water-Temperature Prediction for Reservoir Construction Project

AbstractIt is the important technical support for technology appraisal to establish technology review system of water-temperature prediction for reservoir construction project. In this study, the technical route, implementation method and process, the required basic data, and key issues were proposed for the water-temperature technology review. The realization of water-temperature technology review can provide technical guarantee for regulating the technical requirements on water temperature prediction in environment impact assessment (EIA) report. Technology review can also prevent arbitrariness in some EIA reports. Moreover technology review could resolve some experts doubts on the prediction result during the process of technology appraisal

Knowledge-aware Deep Framework for Collaborative Skin Lesion Segmentation and Melanoma Recognition

Deep learning techniques have shown their superior performance in dermatologist clinical inspection. Nevertheless, melanoma diagnosis is still a challenging task due to the difficulty of incorporating the useful dermatologist clinical knowledge into the learning process. In this paper, we propose a novel knowledge-aware deep framework that incorporates some clinical knowledge into collaborative learning of two important melanoma diagnosis tasks, i.e., skin lesion segmentation and melanoma recognition. Specifically, to exploit the knowledge of morphological expressions of the lesion region and also the periphery region for melanoma identification, a lesion-based pooling and shape extraction (LPSE) scheme is designed, which transfers the structure information obtained from skin lesion segmentation into melanoma recognition. Meanwhile, to pass the skin lesion diagnosis knowledge from melanoma recognition to skin lesion segmentation, an effective diagnosis guided feature fusion (DGFF) strategy is designed. Moreover, we propose a recursive mutual learning mechanism that further promotes the inter-task cooperation, and thus iteratively improves the joint learning capability of the model for both skin lesion segmentation and melanoma recognition. Experimental results on two publicly available skin lesion datasets show the effectiveness of the proposed method for melanoma analysis.Comment: Pattern Recognitio

Pricing Decision under Dual-Channel Structure considering Fairness and Free-Riding Behavior

Under dual-channel structure, the free-riding behavior based on different service levels between online channel and offline channel cannot be avoided, which would lead to channel unfairness. This study implies that the dual-channel supply chain is built up by online channel controlled by manufacturer and traditional channel controlled by retailer, respectively. Under this channel structure, we rebuild the linear demand function considering free-riding behavior and modify the pricing model based on channel fairness. Then the influences of fair factor and free-riding behavior on manufacturer and retailer pricing and performance are discussed. Finally, we propose some numerical analysis to provide some valuable recommendations for manufacturer and retailer improving channel management performance