Our extensive experimental results on two well-established public benchmark datasets demonstrate that our method is more effective than other state-of-the-art methods for skin lesion segmentation.

The segmentation of skin lesions in dermoscopic images is a fundamental step in automated computer-aided diagnosis of melanoma. Conventional segmentation methods, however, have difficulties when the lesion borders are indistinct and when contrast between the lesion and the surrounding skin is low. They also perform poorly when there is a heterogeneous background or a lesion that touches the image boundaries; this then results in under- and oversegmentation of the skin lesion. We suggest that saliency detection using the reconstruction errors derived from a sparse representation model coupled with a novel background detection can more accurately discriminate the lesion from surrounding regions. We further propose a Bayesian framework that better delineates the shape and boundaries of the lesion. We also evaluated our approach on two public datasets comprising 1100 dermoscopic images and compared it to other conventional and state-of-the-art unsupervised (i.e., no training required) lesion segmentation methods, as well as the state-of-the-art unsupervised saliency detection methods. Our results show that our approach is more accurate and robust in segmenting lesions compared to other methods. We also discuss the general extension of our framework as a saliency optimization algorithm for lesion segmentation.

The segmentation of abnormal regions on dermoscopic images is an important step for automated computer aided diagnosis (CAD) of skin lesions. Recent methods based on fully convolutional networks (FCN) have been very successful for dermoscopic image segmentation. However, they tend to overfit to the visual characteristics that are present in the dominant non-melanoma studies and therefore, perform poorly on the complex visual characteristics exhibited by melanoma studies, which usually consists of fuzzy boundaries and heterogeneous textures. In this paper, we propose a new method for automated skin lesion segmentation that overcomes these limitations via a novel deep class-specific learning approach which learns the important visual characteristics of the skin lesions of each individual class (melanoma vs non-melanoma) on an individual basis. We also introduce a new probability-based, step-wise integration to combine complementary segmentation results derived from individual class-specific learning models. We achieved an average Dice coefficient of 85.66% on the ISBI 2017 Skin Lesion Challenge (SLC), 91.77% on the ISBI 2016 SLC and 92.10% on the PH2 datasets with corresponding Jaccard indices of 77.73%, 85.92% and 85.90%, respectively, for the same datasets. Our

Abstract. Positron emission tomography (PET) imaging is widely used for staging and monitoring treatment in a variety of cancers including the lymphomas and lung cancer. Recently, there has been a marked increase in the accuracy and robustness of machine learning methods and their application to computer-aided diagnosis (CAD) systems, e.g., the automated detection and quantification of abnormalities in medical images. Successful machine learning methods require large amounts of training data and hence, synthesis of PET images could play an important role in enhancing training data and ultimately improve the accuracy of PETbased CAD systems. Existing approaches such as atlas-based or methods that are based on simulated or physical phantoms have problems in synthesizing the low resolution and low signal-to-noise ratios inherent in PET images. In addition, these methods usually have limited capacity to produce a variety of synthetic PET images with large anatomical and functional differences. Hence, we propose a new method to synthesize PET data via multi-channel generative adversarial networks (M-GAN) to address these limitations. Our M-GAN approach, in contrast to the existing medical image synthetic methods that rely on using low-level features, has the ability to capture feature representations with a highlevel of semantic information based on the adversarial learning concept. Our M-GAN is also able to take the input from the annotation (label) to synthesize regions of high uptake e.g., tumors and from the computed tomography (CT) images to constrain the appearance consistency based on the CT derived anatomical information in a single framework and output the synthetic PET images directly. Our experimental data from 50 lung cancer PET-CT studies show that our method provides more realistic PET images compared to conventional GAN methods. Further, the PET tumor detection model, trained with our synthetic PET data, performed competitively when compared to the detection model trained with real PET data (2.79% lower in terms of recall). We suggest that our approach when used in combination with real and synthetic images, boosts the training data for machine learning methods.

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