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Two-Stage Deep Neural Network via Ensemble Learning for Melanoma Classification

#artificialintelligence

Melanoma is a skin disease with a high fatality rate. Early diagnosis of melanoma can effectively increase the survival rate of patients. There are three types of dermoscopy images, malignant melanoma, benign nevis, and seborrheic keratosis, so using dermoscopy images to classify melanoma is an indispensable task in diagnosis. However, early melanoma classification works can only use the low-level information of images, so the melanoma cannot be classified efficiently; the recent deep learning methods mainly depend on a single network, although it can extract high-level features, the poor scale and type of the features limited the results of the classification. Therefore, we need an automatic classification method for melanoma, which can make full use of the rich and deep feature information of images for classification. In this study, we propose an ensemble method that can integrate different types of classification networks for melanoma classification. Specifically, we first use U-net to segment the lesion area of images to generate a lesion mask, thus resize images to focus on the lesion; then, we use five excellent classification models to classify dermoscopy images, and adding squeeze-excitation block (SE block) to models to emphasize the more informative features; finally, we use our proposed new ensemble network to integrate five different classification results. The experimental results prove the validity of our results. We test our method on the ISIC 2017 challeng...


Assessing the Impact of Attention and Self-Attention Mechanisms on the Classification of Skin Lesions

arXiv.org Artificial Intelligence

Attention mechanisms have raised significant interest in the research community, since they promise significant improvements in the performance of neural network architectures. However, in any specific problem, we still lack a principled way to choose specific mechanisms and hyper-parameters that lead to guaranteed improvements. More recently, self-attention has been proposed and widely used in transformer-like architectures, leading to significant breakthroughs in some applications. In this work we focus on two forms of attention mechanisms: attention modules and self-attention. Attention modules are used to reweight the features of each layer input tensor. Different modules have different ways to perform this reweighting in fully connected or convolutional layers. The attention models studied are completely modular and in this work they will be used with the popular ResNet architecture. Self-Attention, originally proposed in the area of Natural Language Processing makes it possible to relate all the items in an input sequence. Self-Attention is becoming increasingly popular in Computer Vision, where it is sometimes combined with convolutional layers, although some recent architectures do away entirely with convolutions. In this work, we study and perform an objective comparison of a number of different attention mechanisms in a specific computer vision task, the classification of samples in the widely used Skin Cancer MNIST dataset. The results show that attention modules do sometimes improve the performance of convolutional neural network architectures, but also that this improvement, although noticeable and statistically significant, is not consistent in different settings. The results obtained with self-attention mechanisms, on the other hand, show consistent and significant improvements, leading to the best results even in architectures with a reduced number of parameters.


Two-stage Deep Neural Network via Ensemble Learning for Melanoma Classification

#artificialintelligence

Melanoma is a skin disease with a high fatality rate. Early diagnosis of melanoma can effectively increase the survival rate of patients. There are three types of dermoscopy images, malignant melanoma, benign nevis and seborrheic keratosis, so using dermoscopy images to classify melanoma is an indispensable task in diagnosis. However, early melanoma classification works can only use the low-level information of images, so the melanoma cannot be classified efficiently; and the recent deep learning methods mainly depend on a single network, although it can extract high-level features, the poor scale and type of the features limited the results of the classification. Therefore, we need an automatic classification method for melanoma, which can make full use of the rich and deep feature information of images for classification. In this study, we propose an ensemble method that can integrate different types of classification networks for melanoma classification. Specifically, we first use U-net to segment the lesion area of images to generate a lesion mask, thus resize images to focus on the lesion; then, we use five excellent classification models to classify dermoscopy images, and adding squeeze-excitation block (SE block) to models to emphasize the informative features; finally we use our proposed new ensemble network to integrate five different classification results. The experimental results prove the validity of our results. We test our method on the ISIC $2017$ challenge...


An artificial intelligence tool that can help detect melanoma

#artificialintelligence

DCNNs are neural networks that can be used to classify (or "name") images to then cluster them (such as when performing a photo search). These machine learning algorithms belong to the subset of deep learning. Using cameras to take wide-field photographs of large areas of patients' bodies, the program uses DCNNs to quickly and effectively identify and screen for early-stage melanoma, according to Luis R. Soenksen, a postdoc and a medical device expert currently acting as MIT's first Venture Builder in Artificial Intelligence and Healthcare. Soenksen conducted the research with MIT researchers, including MIT Institute for Medical Engineering and Science (IMES) faculty members Martha J. Gray, W. Kieckhefer Professor of Health Sciences and Technology, professor of electrical engineering and computer science; and James J. Collins, Termeer Professor of Medical Engineering and Science and Biological Engineering. Soenksen, who is the first author of the recent paper, "Using Deep Learning for Dermatologist-level Detection of Suspicious Pigmented Skin Lesions from Wide-field Images," published in Science Translational Medicine, explains that "Early detection of SPLs can save lives; however, the current capacity of medical systems to provide comprehensive skin screenings at scale are still lacking."


An Encoder-Decoder CNN for Hair Removal in Dermoscopic Images

arXiv.org Artificial Intelligence

The process of removing occluding hair has a relevant role in the early and accurate diagnosis of skin cancer. It consists of detecting hairs and restore the texture below them, which is sporadically occluded. In this work, we present a model based on convolutional neural networks for hair removal in dermoscopic images. During the network's training, we use a combined loss function to improve the restoration ability of the proposed model. In order to train the CNN and to quantitatively validate their performance, we simulate the presence of skin hair in hairless images extracted from publicly known datasets such as the PH2, dermquest, dermis, EDRA2002, and the ISIC Data Archive. As far as we know, there is no other hair removal method based on deep learning. Thus, we compare our results with six state-of-the-art algorithms based on traditional computer vision techniques by means of similarity measures that compare the reference hairless image and the one with hair simulated. Finally, a statistical test is used to compare the methods. Both qualitative and quantitative results demonstrate the effectiveness of our network.


The impact of patient clinical information on automated skin cancer detection

arXiv.org Machine Learning

Skin cancer is one of the most common types of cancer around the world. For this reason, over the past years, different approaches have been proposed to assist detect it. Nonetheless, most of them are based only on dermoscopy images and do not take into account the patient clinical information. In this work, first, we present a new dataset that contains clinical images, acquired from smartphones, and patient clinical information of the skin lesions. Next, we introduce a straightforward approach to combine the clinical data and the images using different well-known deep learning models. These models are applied to the presented dataset using only the images and combining them with the patient clinical information. We present a comprehensive study to show the impact of the clinical data on the final predictions. The results obtained by combining both sets of information show a general improvement of around 7% in the balanced accuracy for all models. In addition, the statistical test indicates significant differences between the models with and without considering both data. The improvement achieved shows the potential of using patient clinical information in skin cancer detection and indicates that this piece of information is important to leverage skin cancer detection systems.


Teaching AI to Explain its Decisions Using Embeddings and Multi-Task Learning

arXiv.org Artificial Intelligence

Using machine learning in high-stakes applications often requires predictions to be accompanied by explanations comprehensible to the domain user, who has ultimate responsibility for decisions and outcomes. Recently, a new framework for providing explanations, called TED, has been proposed to provide meaningful explanations for predictions. This framework augments training data to include explanations elicited from domain users, in addition to features and labels. This approach ensures that explanations for predictions are tailored to the complexity expectations and domain knowledge of the consumer. In this paper, we build on this foundational work, by exploring more sophisticated instantiations of the TED framework and empirically evaluate their effectiveness in two diverse domains, chemical odor and skin cancer prediction. Results demonstrate that meaningful explanations can be reliably taught to machine learning algorithms, and in some cases, improving modeling accuracy.


Dermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms

arXiv.org Machine Learning

In this paper, the effectiveness and capability of convolutional neural networks have been studied in the classification of 8 skin diseases. Different pre-trained state-of-the-art architectures (DenseNet 201, ResNet 152, Inception v3, InceptionResNet v2) were used and applied on 10135 dermoscopy skin images in total (HAM10000: 10015, PH2: 120). The utilized dataset includes 8 diagnostic categories - melanoma, melanocytic nevi, basal cell carcinoma, benign keratosis, actinic keratosis and intraepithelial carcinoma, dermatofibroma, vascular lesions, and atypical nevi. The aim is to compare the ability of deep learning with the performance of highly trained dermatologists. Overall, the mean results show that all deep learning models outperformed dermatologists (at least 11%). The best ROC AUC values for melanoma and basal cell carcinoma are 94.40% (ResNet 152) and 99.30% (DenseNet 201) versus 82.26% and 88.82% of dermatologists, respectively. Also, DenseNet 201 had the highest macro and micro averaged AUC values for overall classification (98.16%, 98.79%, respectively).


Artificial intelligence algorithms appear to be better at detecting skin cancer

#artificialintelligence

Researchers have shown for the first time that a form of artificial intelligence or machine learning known as a deep learning convolutional neural network (CNN) is better than experienced dermatologists at detecting skin cancer. In a study published in the leading cancer journal Annals of Oncology today (Tuesday), researchers in Germany, the USA and France trained a CNN to identify skin cancer by showing it more than 100,000 images of malignant melanomas (the most lethal form of skin cancer), as well as benign moles (or nevi). They compared its performance with that of 58 international dermatologists and found that the CNN missed fewer melanomas and misdiagnosed benign moles less often as malignant than the group of dermatologists. A CNN is an artificial neural network inspired by the biological processes at work when nerve cells (neurons) in the brain are connected to each other and respond to what the eye sees. The CNN is capable of learning fast from images that it "sees" and teaching itself from what it has learned to improve its performance (a process known as machine learning).


Artificial Intelligence Better Than Doctors At Diagnosing Skin Cancer

#artificialintelligence

Skin cancer was found to be diagnosed more accurately by artificial intelligence than experienced dermatologists in a new international study. Researchers tested a form of machine learning known as a deep learning convolutional neural network (CNN) to reach this conclusion. The study titled "Artificial intelligence for melanoma diagnosis: How can we deliver on the promise?" was published in the cancer journal Annals of Oncology on May 28. Malignant melanoma accounts for 1 percent of all skin cancers but causes a majority of skin cancer-related deaths. The American Cancer Society estimates 9,320 people will die from melanoma in 2018 while 91,270 new cases will be diagnosed.