Fusing fine-tuned deep features for skin lesion classification

© 2018 Elsevier Ltd Malignant melanoma is one of the most aggressive forms of skin cancer. Early detection is important as it significantly improves survival rates. Consequently, accurate discrimination of malignant skin lesions from benign lesions such as seborrheic keratoses or benign nevi is crucial, while accurate computerised classification of skin lesion images is of great interest to support diagnosis. In this paper, we propose a fully automatic computerised method to classify skin lesions from dermoscopic images. Our approach is based on a novel ensemble scheme for convolutional neural networks (CNNs) that combines intra-architecture and inter-architecture network fusion. The proposed method consists of multiple sets of CNNs of different architecture that represent different feature abstraction levels. Each set of CNNs consists of a number of pre-trained networks that have identical architecture but are fine-tuned on dermoscopic skin lesion images with different settings. The deep features of each network were used to train different support vector machine classifiers. Finally, the average prediction probability classification vectors from different sets are fused to provide the final prediction. Evaluated on the 600 test images of the ISIC 2017 skin lesion classification challenge, the proposed algorithm yields an area under receiver operating characteristic curve of 87.3% for melanoma classification and an area under receiver operating characteristic curve of 95.5% for seborrheic keratosis classification, outperforming the top-ranked methods of the challenge while being simpler compared to them. The obtained results convincingly demonstrate our proposed approach to represent a reliable and robust method for feature extraction, model fusion and classification of dermoscopic skin lesion images

Transfer learning using a multi-scale and multi-network ensemble for skin lesion classification

Background and objective: Skin cancer is among the most common cancer types in the white population and consequently computer aided methods for skin lesion classification based on dermoscopic images are of great interest. A promising approach for this uses transfer learning to adapt pre-trained convolutional neural networks (CNNs) for skin lesion diagnosis. Since pre-training commonly occurs with natural images of a fixed image resolution and these training images are usually significantly smaller than dermoscopic images, downsampling or cropping of skin lesion images is required. This however may result in a loss of useful medical information, while the ideal resizing or cropping factor of dermoscopic images for the fine-tuning process remains unknown. Methods: We investigate the effect of image size for skin lesion classification based on pre-trained CNNs and transfer learning. Dermoscopic images from the International Skin Imaging Collaboration (ISIC) skin lesion classification challenge datasets are either resized to or cropped at six different sizes ranging from 224 × 224 to 450 × 450. The resulting classification performance of three well established CNNs, namely EfficientNetB0, EfficientNetB1 and SeReNeXt-50 is explored. We also propose and evaluate a multi-scale multi-CNN (MSM-CNN) fusion approach based on a three-level ensemble strategy that utilises the three network architectures trained on cropped dermoscopic images of various scales. Results: Our results show that image cropping is a better strategy compared to image resizing delivering superior classification performance at all explored image scales. Moreover, fusing the results of all three fine-tuned networks using cropped images at all six scales in the proposed MSM-CNN approach boosts the classification performance compared to a single network or a single image scale. On the ISIC 2018 skin lesion classification challenge test set, our MSM-CNN algorithm yields a balanced multi-class accuracy of 86.2% making it the currently second ranked algorithm on the live leaderboard. Conclusions: We confirm that the image size has an effect on skin lesion classification performance when employing transfer learning of CNNs. We also show that image cropping results in better performance compared to image resizing. Finally, a straightforward ensembling approach that fuses the results from images cropped at six scales and three fine-tuned CNNs is shown to lead to the best classification performance

Color-Tunable ZnO/GaN Heterojunction LEDs Achieved by Coupling with Ag Nanowire Surface Plasmons

Color-tunable light-emitting devices (LEDs) have a great impact on our daily life. Herein, LEDs with tunable electroluminescence (EL) color were achieved via introducing Ag nanowires surface plasmons into p-GaN/n-ZnO film heterostructures. By optimizing the surface coverage density of coated Ag nanowires, the EL color was changed continuously from yellow-green to blue-violet. Transient-state and temperature-variable fluorescence emission characterizations uncovered that the spontaneous emission rate and the internal quantum efficiency of the near-UV emission were increased as a consequence of the resonance coupling interaction between Ag nanowires surface plasmons and ZnO excitons. This effect induces the selective enhancement of the blue-violet EL component but suppresses the defect-related yellow-green emission, leading to the observed tunable EL color. The proposed strategy of introducing surface plasmons can be further applied to many other kinds of LEDs for their selective enhancement of EL intensity and effective adjustment of the emission color

Enhanced Ultraviolet Random Lasing from Au/MgO/ZnO Heterostructure by Introducing p‑Cu₂O Hole-Injection Layer

Ultraviolet light-emitting devices (LEDs) were fabricated on the basis of Au/MgO/ZnO metal/insulator/semiconductor (MIS) heterostructures. By introducing a thermally oxidized p-type Cu₂O hole-injection layer into this MIS structure, enhanced ultraviolet electroluminescence (EL) and random lasing with reduced threshold injection current are achieved. The enhancement mechanism is attributed to effective hole transfer from p-Cu₂O to i-MgO under forward bias, which increases the initial carrier concentration of MgO dielectric layer and further promotes “impact-ionization” effect induced carrier generation and injection. The current study proposes a new and effective route to improve the EL performance of MIS junction LEDs via introducing extrinsic hole suppliers

Kaplan-Meier survival curves of gastric adenocarcinoma patients (n = 513) after gastrectomy.

<p>The survival rate of patients in <i>PTPRD</i>-high group was significantly higher than that of the patients in the <i>PTPRD</i>-low group (log-rank test, P<0.001).</p

Univariate and multivariate analyses of overall survival of gastric adenocarcinoma patients.

<p>HR, hazard ratio; CI, confidence interval; ^a Numbers of cases in each group; * Statistically significant (<i>P</i><0.05).</p><p>Univariate and multivariate analyses of overall survival of gastric adenocarcinoma patients.</p

Correlation between <i>PTPRD</i> expression and clinicopathological parameters of 513 gastric adenocarcinoma cases.

a<p>^aNumbers of cases in each group. * Statistically significant (<i>P</i><0.05).</p><p>Correlation between <i>PTPRD</i> expression and clinicopathological parameters of 513 gastric adenocarcinoma cases.</p

The mRNA expression of <i>PTPRD</i> in human primary gastric adenocarcinoma surgical specimens was evaluated by qRT-PCR.

<p>The relative mRNA expression of <i>PTPRD</i> was significantly decreased in GC tissues compared with the matched adjacent noncancerous tissues (n = 42, P = 0.0138). Horizontal lines represent the mean.</p

The growth suppressor role of <i>PTPRD</i> in cell proliferation and DNA methylation analysis of <i>PTPRD</i>.

<p>(A) Western blotting analysis of <i>PTPRD</i> overexpression in MGC803 cells. (B) Western blotting analysis of decreased <i>PTPRD</i> expression in GES1 cells. (C) Cell proliferation assay showing the suppressive effect of restoring <i>PTPRD</i> expression on the proliferation of MGC803 cell line. (D) Results showing significantly enhanced proliferation rate of <i>PTPRD</i>-silenced GES1 cells compared with mock siRNA treatment GES1 cells. (E) Methylation analysis of <i>PTPRD</i> promoter CpG island in primary GC tissues. Among 3 GC samples, one case showed partial methylation and 2 cases were unmethylated. *, P<0.05 versus the mock control; **, P<0.01 versus the mock control.</p

<i>PTPRD</i> protein expression in gastric adenocarcinoma surgical specimens evaluated by immunohistochemistry.

<p>(A) Strong <i>PTPRD</i> staining was observed in noncancerous gastric mucosa. (B) Strong <i>PTPRD</i> staining in well-differentiated gastric cancer. (C) Weak <i>PTPRD</i> staining in moderately differentiated GC. (D) Negative <i>PTPRD</i> staining in poorly differentiated GC. (E) Immunostaining of GC and adjacent nontumorous tissues showing a sharp contrast of <i>PTPRD</i> staining intensity.</p