Efficient Skin Region Segmentation Using Low Complexity Fuzzy Decision Tree Model

Analyzing the Effect of Imputation on Classification Performance under MCAR and MAR Missing Mechanisms

Many datasets in statistical analyses contain missing values. As omitting observations containing missing entries may lead to information loss or greatly reduce the sample size, imputation is usually preferable. However, imputation can also introduce bias and impact the quality and validity of subsequent analysis. Focusing on binary classification problems, we analyzed how missing value imputation under MCAR as well as MAR missingness with different missing patterns affects the predictive performance of subsequent classification. To this end, we compared imputation methods such as several MICE variants, missForest, Hot Deck as well as mean imputation with regard to the classification performance achieved with commonly used classifiers such as Random Forest, Extreme Gradient Boosting, Support Vector Machine and regularized logistic regression. Our simulation results showed that Random Forest based imputation (i.e., MICE Random Forest and missForest) performed particularly well in most scenarios studied. In addition to these two methods, simple mean imputation also proved to be useful, especially when many features (covariates) contained missing values.

Fully corrective gradient boosting with squared hinge: Fast learning rates and early stopping

In this paper, we propose an efficient boosting method with theoretical guarantees for binary classification. There are three key ingredients of the proposed boosting method: a fully corrective greedy (FCG) update, a differentiable squared hinge (also called truncated quadratic) loss function, and an efficient alternating direction method of multipliers (ADMM) solver. Compared with traditional boosting methods, on one hand, the FCG update accelerates the numerical convergence rate, and on the other hand, the squared hinge loss inherits the robustness of the hinge loss for classification and maintains the theoretical benefits of the square loss in regression. The ADMM solver with guaranteed fast convergence then provides an efficient implementation for the proposed boosting method. We conduct both theoretical analysis and numerical verification to show the outperformance of the proposed method. Theoretically, a fast learning rate of order O((m/logm)^-1/2) is proved under certain standard assumptions, where m is the size of sample set. Numerically, a series of toy simulations and real data experiments are carried out to verify the developed theory.

Proposing a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) method for localizing vitiligo lesions in skin images

BACKGROUND

Vitiligo is an acquired pigmentary skin disorder characterized by depigmented macules and patches which brings many challenges for the patients suffering from. For vitiligo severity assessment, several scoring methods have been proposed based on morphometry and colorimetry. But, all methods suffer from much inter- and intra-observer variations for estimating the depigmented area. For all mentioned assessment methods of vitiligo disorder, accurate segmentation of the skin images for lesion detection and localization is required. The image segmentation for localizing vitiligo skin lesions has many challenges because of illumination variation, different shapes and sizes of vitiligo lesions, vague lesion boundaries and skin hairs and vignette effects. The manual image segmentation is a tedious and time-consuming task. Therefore, using automatic image segmentation methods for lesion detection is necessarily required.

MATERIALS AND METHODS

In this study, a novel unsupervised stack ensemble of deep and conventional image segmentation (SEDCIS) methods is proposed for localizing vitiligo lesions in skin images. Unsupervised segmentation methods do not require prior manual segmentation of vitiligo lesions which is a tedious and time-consuming task with intra- and inter-observer variations.

RESULTS

Our collected dataset includes 877 images taken from 21 patients with the resolution of 5760*3840 pixels suffering from vitiligo disorder. Experimental results show that SEDCIS outperforms the compared methods with accuracy of 97%, sensitivity of 98%, specificity of 96%, area overlapping of 94%, and Dice index of 97%.

CONCLUSION

The proposed method can segment vitiligo lesions with highly reasonable performance and can be used for assessing the vitiligo lesion surface.