USL-Net: Uncertainty Self-Learning Network for Unsupervised Skin Lesion Segmentation

Unsupervised skin lesion segmentation offers several benefits, including conserving expert human resources, reducing discrepancies due to subjective human labeling, and adapting to novel environments. However, segmenting dermoscopic images without manual labeling guidance presents significant challenges due to dermoscopic image artifacts such as hair noise, blister noise, and subtle edge differences. To address these challenges, we introduce an innovative Uncertainty Self-Learning Network (USL-Net) designed for skin lesion segmentation. The USL-Net can effectively segment a range of lesions, eliminating the need for manual labeling guidance. Initially, features are extracted using contrastive learning, followed by the generation of Class Activation Maps (CAMs) as saliency maps using these features. The different CAM locations correspond to the importance of the lesion region based on their saliency. High-saliency regions in the map serve as pseudo-labels for lesion regions while low-saliency regions represent the background. However, intermediate regions can be hard to classify, often due to their proximity to lesion edges or interference from hair or blisters. Rather than risk potential pseudo-labeling errors or learning confusion by forcefully classifying these regions, we consider them as uncertainty regions, exempting them from pseudo-labeling and allowing the network to self-learn. Further, we employ connectivity detection and centrality detection to refine foreground pseudo-labels and reduce noise-induced errors. The application of cycle refining enhances performance further. Our method underwent thorough experimental validation on the ISIC-2017, ISIC-2018, and PH2 datasets, demonstrating that its performance is on par with weakly supervised and supervised methods, and exceeds that of other existing unsupervised methods.Comment: 14 pages, 9 figures, 71 reference

A Review on Skin Disease Classification and Detection Using Deep Learning Techniques

Skin cancer ranks among the most dangerous cancers. Skin cancers are commonly referred to as Melanoma. Melanoma is brought on by genetic faults or mutations on the skin, which are caused by Unrepaired Deoxyribonucleic Acid (DNA) in skin cells. It is essential to detect skin cancer in its infancy phase since it is more curable in its initial phases. Skin cancer typically progresses to other regions of the body. Owing to the disease's increased frequency, high mortality rate, and prohibitively high cost of medical treatments, early diagnosis of skin cancer signs is crucial. Due to the fact that how hazardous these disorders are, scholars have developed a number of early-detection techniques for melanoma. Lesion characteristics such as symmetry, colour, size, shape, and others are often utilised to detect skin cancer and distinguish benign skin cancer from melanoma. An in-depth investigation of deep learning techniques for melanoma's early detection is provided in this study. This study discusses the traditional feature extraction-based machine learning approaches for the segmentation and classification of skin lesions. Comparison-oriented research has been conducted to demonstrate the significance of various deep learning-based segmentation and classification approaches

Skin Lesion Area Segmentation Using Attention Squeeze U-Net for Embedded Devices

Melanoma is the deadliest form of skin cancer. Early diagnosis of malignant lesions is crucial for reducing mortality. The use of deep learning techniques on dermoscopic images can help in keeping track of the change over time in the appearance of the lesion, which is an important factor for detecting malignant lesions. In this paper, we present a deep learning architecture called Attention Squeeze U-Net for skin lesion area segmentation specifically designed for embedded devices. The main goal is to increase the patient empowerment through the adoption of deep learning algorithms that can run locally on smartphones or low cost embedded devices. This can be the basis to (1) create a history of the lesion, (2) reduce patient visits to the hospital, and (3) protect the privacy of the users. Quantitative results on publicly available data demonstrate that it is possible to achieve good segmentation results even with a compact model

Сегментация дерматоскопических изображений новообразований кожи. Сравнение методик

В работе рассмотрен ряд методик сегментации дерматоскопических изображений новообразований кожи для выявления областей, занимаемых данными новообразованиями. Выполнение сегментации необходимо как первый этап большинства методик компьютерной диагностики злокачественности новообразований. Ряд методик, таких как ABCDE, используют форму новообразования как один из критериев постановки диагноза, для других, таких как использование сверточных нейроных сетей, выделение новообразования позволяет повысить точность получаемых результатов. В работе рассмотрены три способа сегментации: пороговая обработка с использованием метода Оцу для вычисления величины порога, сверточная нейронная сеть, построеннная по архитектуре U-net, и аналогичная сверточная нейронная сеть с добавленным механизмом внимания. Рассмотрены достоинства и недостатки каждой из методик, а также возможности совместного их применения для получения наилучших результатов сегментации

Segmentación de imágenes dermoscópicas para detección de melanomas

El melanoma es el cáncer de piel que más muertes produce al año en el mundo y su incidencia en los últimos años ha aumentado considerablemente. El desarrollo de software que facilite su detección en los estadios tempranos de la enfermedad puede ayudar a reducir la mortalidad y morbilidad asociadas. El grupo de Aplicaciones Multimedia y Acústica (GAMMA) de la Universidad Politécnica de Madrid, lleva desde 2016 desarrollando algoritmos para segmentar y clasificar lesiones melanocíticas en imágenes dermatoscópicas. En 2017, se colaboró en un algoritmo basado en umbralización y se comenzaron a realizar pruebas para segmentar imágenes dermoscópicas empleando otras técnicas. En este proyecto se propone un método de segmentación de imágenes dermoscópicas para detección de melanomas basado en Watershed. Se parte de una aplicación ya existente, para la segmentación del área glotal en imágenes tomadas de videos estroboscópicos. Para adaptar y modificar la aplicación de partida, se ha empleado el entorno Visual Studio y el lenguaje de programación C#. La aplicación ofrece una interfaz de usuario en la que se pueden ajustar ciertos parámetros para optimizar la segmentación. El algoritmo propuesto introduce modificaciones para aislar la lesión en la piel. Para ello, y partiendo de las regiones segmentadas mediante Watershed, se establecen criterios, basados en el espacio de color CIELAB y YIQ para determinar si esas regiones son clasificadas como lesión o como fondo. Además, se ha determinado el valor de los parámetros de entrada a la aplicación que optimicen la segmentación. La aplicación ofrece como salida una máscara binaria, para poder compararla, con la segmentación proporcionada por los expertos. Tras procesar las 2000 imágenes de la base de datos ISIC, se ha obtenido un índice Jaccard de 0.61. Las imágenes que obtienen un Jaccard superior a 0.8 son aproximadamente 750. Y casi la mitad del total superan el 0.70. La aplicación propuesta no realiza ningún tipo de preprocesado sobre las imágenes originales, ni postprocesado sobre las máscaras binarias obtenidas. Sin embargo, se han realizado pruebas combinando el algoritmo propuesto con dos algoritmos desarrollados por el grupo GAMMA. El primero, realiza un preprocesado para eliminar pelos de algunas imágenes, y el segundo, pretende eliminar los objetos de la máscara binaria identificados erróneamente como lesión. El mejor resultado obtenido para el índice Jaccard, en este caso, es de 0.63. En el proyecto se ha conseguido aplicar la transformada Watershed a la segmentación de imágenes dermatoscópicas sin adaptar los parámetros de la aplicación a cada imagen. Los resultados son muy prometedores no solo para segmentar la lesión en la imagen sino también para segmentar las estructuras internas de la lesión. Abstract: Melanoma is the skin cancer that causes most of the deaths per year in the world, and its incidence has increased considerably in recent years. Detection of the disease at early stages can reduce the associated mortality and morbidity. Computer Aided Diagnosis (CAD) tools can help in this task. The Acoustics and Multimedia Applications (GAMMA) group of the Polytechnic University of Madrid has been developing algorithms to segment and classify melanocytic lesions in dermatoscopic images since 2016. In 2017, we collaborated on an algorithm based on thresholding and began segmenting dermoscopic images using other techniques. This project proposes a method of segmentation of dermoscopic images for the detection of melanomas based on Watershed Transform. The original system segments the glottal area in images taken from stroboscopic videos. To adapt and modify the original application, we have used Visual Studio environment and C# programming language. The app provides a user interface in which certain parameters can be adjusted to optimize segmentation. The proposed algorithm introduces modifications to isolate the lesion on the skin. To do this and starting from the regions segmented by Watershed, criteria are established, based on the color space CIELAB and YIQ, to determine if these regions are classified as lesion or as background. In addition, the value of the input parameters to the application that optimize segmentation has been determined. The application offers as output a binary mask to be able to compare it with the segmentation provided by the experts. After processing the 2000 images from the ISIC database, a Jaccard index of 0.61 was obtained. The images that get a Jaccard greater than 0.8 are approximately 750. And nearly half of the total exceeds 0.70 The proposed application does not perform any kind of preprocessing on the original images, nor post-processing on the binary masks obtained. However, tests have been conducted combining the proposed algorithm with two algorithms developed by the GAMMA group. The first, performs a preprocessing to remove hairs from some images, and the second, aims to remove objects from the binary mask misidentified as lesion. In this case, the best result obtained for the Jaccard index is 0.63. The project has managed to apply the Watershed transform to dermoscopic image segmentation without adapting the application parameters to each image. The results are very promising not only for segmenting the lesion in the image but also for segmenting the internal structures of the lesion