Analysis of the ISIC image datasets: Usage, benchmarks and recommendations

The International Skin Imaging Collaboration (ISIC) datasets have become a leading repository for researchers in machine learning for medical image analysis, especially in the field of skin cancer detection and malignancy assessment. They contain tens of thousands of dermoscopic photographs together with gold-standard lesion diagnosis metadata. The associated yearly challenges have resulted in major contributions to the field, with papers reporting measures well in excess of human experts. Skin cancers can be divided into two major groups - melanoma and non-melanoma. Although less prevalent, melanoma is considered to be more serious as it can quickly spread to other organs if not treated at an early stage. In this paper, we summarise the usage of the ISIC dataset images and present an analysis of yearly releases over a period of 2016 - 2020. Our analysis found a significant number of duplicate images, both within and between the datasets. Additionally, we also noted duplicates spread across testing and training sets. Due to these irregularities, we propose a duplicate removal strategy and recommend a curated dataset for researchers to use when working on ISIC datasets. Given that ISIC 2020 focused on melanoma classification, we conduct experiments to provide benchmark results on the ISIC 2020 test set, with additional analysis on the smaller ISIC 2017 test set. Testing was completed following the application of our duplicate removal strategy and an additional data balancing step. As a result of removing 14,310 duplicate images from the training set, our benchmark results show good levels of melanoma prediction with an AUC of 0.80 for the best performing model. As our aim was not to maximise network performance, we did not include additional steps in our experiments. Finally, we provide recommendations for future research by highlighting irregularities that may present research challenges. A list of image files with reference to the original ISIC dataset sources for the recommended curated training set will be shared on our GitHub repository (available at www.github.com/mmu-dermatology-research/isic_duplicate_removal_strategy)

Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given

Open source software for the analysis of corneal deformation parameters on the images from the Corvis tonometer

Background: The software supplied with the Corvis tonometer (which is designed to measure intraocular pressure with the use of the air-puff method) is limited to providing basic numerical data. These data relate to the values of the measured intraocular pressure and, for example, applanation amplitudes. However, on the basis of a sequence of images obtained from the Corvis tonometer, it is possible to obtain much more information which is not available in its original software. This will be presented in this paper. Material and method: The proposed software has been tested on 1400 images from the Corvis tonometer. The number of analysed 2D images (with a resolution of 200×576 pixels) in a sequence is arbitrary. However, in typical cases there are 140 images. The proposed software has been written in Matlab (Version 7.11.0.584, R2010b). The methods of image analysis and processing and in particular edge detection and the fast Fourier transform have been applied. Results and discussion: The software allows for fully automatic (1) acquisition of 12 new parameters previously unavailable in the original software of the Corvis tonometer. It also enables off-line (2) manual and (3) automatic browsing of images in a sequence; 3D graph visualization of: (4) the corneal deformation and (5) eyeball response; 6) change of the colour palette; (7) filtration and (8) visualization of selected measured values on individual 2D images. In addition, the proposed software enables (9) to save the obtained results for further analysis and processing. Conclusions: The dedicated software described in this paper enables to obtain additional new features of corneal deformations during intraocular pressure measurement. The software can be applied in the diagnosis of corneal deformation vibrations, glaucoma diagnosis, evaluation of measurement repeatability and others. The software has no licensing restrictions and can be used both commercially and non-commercially without any limitations