Skin biopsy histopathological analysis is one of the primary methods used for pathologists to assess the presence and deterioration of melanoma in clinical. A comprehensive and reliable pathological analysis is the result of correctly segmented melanoma and its interaction with benign tissues, and therefore providing accurate therapy. In this study, we applied the deep convolution network on the hyperspectral pathology images to perform the segmentation of melanoma. To make the best use of spectral properties of three dimensional hyperspectral data, we proposed a 3D fully convolutional network named Hyper-net to segment melanoma from hyperspectral pathology images. In order to enhance the sensitivity of the model, we made a specific modification to the loss function with caution of false negative in diagnosis. The performance of Hyper-net surpassed the 2D model with the accuracy over 92%. The false negative rate decreased by nearly 66% using Hyper-net with the modified loss function. These findings demonstrated the ability of the Hyper-net for assisting pathologists in diagnosis of melanoma based on hyperspectral pathology images.

Hyperspectral image classification is an important part of the hyperspectral remote sensing information processing. The Iterative Selforganizing Data Analysis Techniques Algorithm (ISODATA) clustering algorithm which is an unsupervised classification algorithm is considered as an effective measure in the area of processing hyperspectral images. In this paper, an improved ISODATA algorithm is proposed for hyperspectral images classification. The algorithm takes the maximum and minimum spectrum of the image into consideration and determines by the stepped construction of spectrum accurately. The classification experiment results show that using the improved ISODATA algorithm can determine the initial cluster number adaptively. In comparison with the SAM (Spectral Angle Mapper) algorithm and the original ISODATA algorithm, a better performance of the proposed ISODATA method is shown in the part of results.

Apple is considered the most commonly grown fruit crop in temperate regions that brings great economic profits to fruit growers. Dwarfing rootstocks have been extensively used in apple breeding as well as commercial orchards, but the molecular and genetic basis of scion dwarfing and other morphological traits induced by them is still unclear. At present, we report a genetic map of Malusdomestica × Malus baccata with high density. The F₁ population was sequenced by a specific length amplified fragment (SLAF). In the genetic map, 5064 SLAF markers spanning 17 linkage groups (LG) were included. Dwarf-related and other phenotypic traits of the scion were evaluated over a 3-year growth period. Based on quantitative trait loci (QTL) evaluation of plant height and trunk diameter, two QTL clusters were found on LG 11, which exhibited remarkable influences on dwarfing of the scion. In this analysis, QTL DW2, which was previously reported as a locus that controls dwarfing, was confirmed. Moreover, three novel QTLs for total flower number and branching flower number were detected on LG2 and LG4, exhibited the phenotypic variation that has been explained by QTL ranging from 8.80% to 34.80%. The findings of the present study are helpful to find scion dwarfing and other phenotypes induced by rootstock in the apple.

Automatic profiled fibre recognition and analysis can accelerate quality inspection and contributes to the upgrade of the textile industry. However, these tasks often require significant manual effort to generate instance‐level annotations for fully supervised training. In this paper, the authors propose a weakly supervised pipeline for profiled fibre inspection using electron‐microscopic (EM) images with only image‐level annotations. It automatically identifies fibre instances and estimates shape factors to facilitate fibre quality inspection. As the core of the pipeline, the weakly supervised network (WesNet) is designed to localize hundreds of crowded fibre samples by raw patch generation and fibre sample sifting. Particularly, the composite similarity measurement integrates different patch‐wise similarities, enabling the network to distinguish fibre from background robustly. For quality inspection, the pipeline further analyzes the fibre instances, utilizing several efficient techniques to estimate the shape factors. Experiments on the real fibre electron‐microscopic images demonstrate the efficacy and efficiency of the pipeline. Results show that WesNet outperforms several supervised and weakly supervised methods, including two state‐of‐the‐art weakly supervised networks.

In this paper, aiming at the positioning and quantitative evaluation of grounding grid corrosion, fracture and other faults in power system, the evaluation methods of grounding grid defects are summarized and analyzed. The current applied or potential diagnosis methods are divided into two categories, one is non imaging grounding grid state detection method, and the other is imaging grounding grid state detection method. The current status of substation grounding grid defect diagnosis and evaluation methods is discussed. At the same time, the development trend of substation grounding grid condition evaluation method is prospected. It is pointed out that it is of great significance to accurately obtain high-resolution characteristic parameter image reflecting grounding grid fault, and the difficulties to realize high-resolution image reconstruction of grounding grid are proposed. The imaging detection method of grounding grid state is very worthy of study, which will bring a new and effective method for grounding grid state diagnosis.