Rahil Garnavi

Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images

This work presents an approach for melanoma recognition in dermoscopy images that combines deep learning, sparse coding, and support vector machine SVM learning algorithms. One of the beneficial aspects of the proposed approach is that unsupervised learning within the domain, and feature transfer from the domain of natural photographs, eliminates the need of annotated data in the target task to learn good features. The applied feature transfer also allows the system to draw analogies between observations in dermoscopic images and observations in the natural world, mimicking the process clinical experts themselves employ to describe patterns in skin lesions. To evaluate the methodology, performance is measured on a dataset obtained from the International Skin Imaging Collaboration, containing 2624 clinical cases of melanoma 334, atypical nevi 144, and benign lesions 2146. The approach is compared to the prior state-of-art method on this dataset. Two-fold cross-validation is performed 20 times for evaluation 40 total experiments, and two discrimination tasks are examined: 1 melanoma vs. all non-melanoma lesions, and 2 melanoma vs. atypical lesions only. The presented approach achieves an accuracy of 93.1% 94.9% sensitivity, and 92.8% specificity for the first task, and 73.9% accuracy 73.8% sensitivity, and 74.3% specificity for the second task. In comparison, prior state-of-art ensemble modeling approaches alone yield 91.2% accuracy 93.0% sensitivity, and 91.0% specificity first the first task, and 71.5% accuracy 72.7% sensitivity, and 68.9% specificity for the second. Differences in performance were statistically significant p

Classification of dermoscopy patterns using deep convolutional neural networks

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Image Super Resolution Using Generative Adversarial Networks and Local Saliency Maps for Retinal Image Analysis

0.05, suggesting the proposed approach is an effective improvement over prior state-of-art.

Exploiting local and generic features for accurate skin lesions classification using clinical and dermoscopy imaging

In this work, we study the performance of a two-stage ensemble visual machine learning framework for classification of medical images. In the first stage, models are built for subsets of features and data, and in the second stage, models are combined. We demonstrate the performance of this framework in four contexts: 1) The public ImageCLEF (Cross Language Evaluation Forum) 2013 medical modality recognition benchmark, 2) echocardiography view and mode recognition, 3) dermatology disease recognition across two datasets, and 4) a broad medical image dataset, merged from multiple data sources into a collection of 158 categories covering both general and specific medical concepts—including modalities, body regions, views, and disease states. In the first context, the presented system achieves state-of-art performance of 82.2% multiclass accuracy. In the second context, the system attains 90.48% multiclass accuracy. In the third, state-of-art performance of 90% specificity and 90% sensitivity is obtained on a small standardized dataset of 200 images using a leave-one-out strategy. For a larger dataset of 2,761 images, 95% specificity and 98% sensitivity is obtained on a 20% held-out test set. Finally, in the fourth context, the system achieves sensitivity and specificity of 94.7% and 98.4%, respectively, demonstrating the ability to generalize over domains.

Skin Hair Removal for 2D Psoriasis Images

Detection of dermoscopic patterns, such as typical network and regular globules, is an important step in the skin lesion analysis. This is one of the steps, required to compute the ABCD-score, commonly used for lesion type classification. In this article, we investigate the possibility of automatically detect dermoscopic patterns using deep convolutional neural networks and other image classification algorithms. For the evaluation, we employ the dataset obtained through collaboration with the International Skin Imaging Collaboration (ISIC), including 211 lesions manually annotated by domain experts, generating over 2000 samples of each class (network and globules). Experimental results demonstrates that we can correctly classify 88% of network examples, and 83% of globules example. The best results are achieved by a convolutional neural network with 8 layers.

Skin Disease Recognition Using Deep Saliency Features and Multimodal Learning of Dermoscopy and Clinical Images

This paper proposes an unsupervised skin lesion segmentation method for dermoscopy images by exploiting the contextual information of skin image at the superpixel level. In particular, a Laplacian sparse coding is presented to evaluate the probabilities of the skin image pixels to delineate lesion border. Moreover, a new rule-based smoothing strategy is proposed as the lesion segmentation refinement procedure. Finally, a multi-scale superpixel segmentation of the skin image is provided to handle size variation of the lesion in order to improve the accuracy of the detected border. Experiments conducted on two datasets show the superiority of our proposed method over several state-of-the-art skin segmentation methods.

A deep bag-of-features model for the classification of melanomas in dermoscopy images

We propose an image super resolution (ISR) method using generative adversarial networks (GANs) that takes a low resolution input fundus image and generates a high resolution super resolved (SR) image upto scaling factor of 16. This facilitates more accurate automated image analysis, especially for small or blurred landmarks and pathologies. Local saliency maps, which define each pixel’s importance, are used to define a novel saliency loss in the GAN cost function. Experimental results show the resulting SR images have perceptual quality very close to the original images and perform better than competing methods that do not weigh pixels according to their importance. When used for retinal vasculature segmentation, our SR images result in accuracy levels close to those obtained when using the original images.

Retinal Image Quality Classification Using Saliency Maps and CNNs

Similarity in appearance between various skin diseases, often makes it challenging for clinicians to identify the type of skin condition, and the accuracy is highly reliant on the level of expertise. There is also a great degree of subjectivity and inter/intra observer variability found in the clinical practices. In this paper, we propose a method for automatic skin diseases recognition that combines two different types of deep convolutional neural network features. We hold the hypothesis that it is equally important to capture global features such as color and lesion shape, as well as local features such as local patterns within the lesion area. The proposed method leverages deep residual network to represent global information, and bilinear pooling technique which allows to extract local features to differentiate between skin conditions with subtle visual differences in local regions. We have evaluated our proposed method on MoleMap dataset with 32,195 and ISBI-2016 challenge dataset with 1,279 skin images. Without any lesion localisation or segmentation, our proposed method has achieved state-of-the-art results on the large-scale MoleMap datasets with 15 various disease categories and multiple imaging modalities, and compares favorably with the best method on ISBI-2016 Melanoma challenge dataset.