Improving skin cancer (melanoma) detection : new method

University of Technology Sydney. Faculty of Engineering and Information Technology.Melanoma, the deadliest form of skin cancer, must be diagnosed early for effective treatment. Rough pigment network and qualities are important signs for melanoma diagnosis using pathologist images. The main focus of this thesis is to improve skin cancer (Melanoma) detection through introducing novel image processing approach for a computer-aided system based on pigment network and elements detection on pathology images. It is important to propose an automated system for differentiating between melanocytic nevi and malignant melanoma. This thesis describes a novel image processing approach for computer-aided pigment network and elements detection on dermoscopy / pathology images. The proposed methods provide meaningful ideas of structures, and extract features for melanoma detection. Additionally, the thesis presents efforts towards prevention of melanoma, by developing a smart system to locate pigment networks. The thesis aims to cover a complete theoretical model for simulating the processes that takes place when a human interprets an image generated by the eye, through designing a reliable system, that can provide a screening method that “filters” lesions and melanoma in a general practice. The proposed system is to be used with a standard PC with input from a high quality digital camera, dermoscopy / microscopy slides or any other suitable hardware sources. This system analyses the structure of a mole / skin defects, detects cancer, identifies features, makes a decision and provides the result. The result of the proposed system shows that the Skin Cancer (Melanoma) Detection strategy which uses SVM performs reasonably satisfactorily (accuracy 77.44%, sensitivity 83.60 %, and specify 70.67%). Furthermore, the SVM based wavelet Gabor (SVM-WLG) performs better than the SVM (81.61%, 88.48%, and 74.51 % accuracy, sensitivity, and specify respectively). However, the Swarm-based SVM (SSVM) performs better than the other two algorithms, with average for accuracy, sensitivity, specificity of 87.13%, 94.1% and 80.22%, respectively

On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors

Deep learning based medical image classifiers have shown remarkable prowess in various application areas like ophthalmology, dermatology, pathology, and radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD) systems in real clinical setups is severely limited primarily because their decision-making process remains largely obscure. This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists. We used a well-trained and high performing neural network developed by REasoning for COmplex Data (RECOD) Lab for classification of three skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and performed a detailed analysis on its latent space. Two well established and publicly available skin disease datasets, PH2 and derm7pt, are used for experimentation. Human understandable concepts are mapped to RECOD image classification model with the help of Concept Activation Vectors (CAVs), introducing a novel training and significance testing paradigm for CAVs. Our results on an independent evaluation set clearly shows that the classifier learns and encodes human understandable concepts in its latent representation. Additionally, TCAV scores (Testing with CAVs) suggest that the neural network indeed makes use of disease-related concepts in the correct way when making predictions. We anticipate that this work can not only increase confidence of medical practitioners on CAD but also serve as a stepping stone for further development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural Networks (IJCNN) 202

Frontiers in Pigment Cell and Melanoma Research

We identify emerging frontiers in clinical and basic research of melanocyte biology and its associated biomedical disciplines. We describe challenges and opportunities in clinical and basic research of normal and diseased melanocytes that impact current approaches to research in melanoma and the dermatological sciences. We focus on four themes: (1) clinical melanoma research, (2) basic melanoma research, (3) clinical dermatology, and (4) basic pigment cell research, with the goal of outlining current highlights, challenges, and frontiers associated with pigmentation and melanocyte biology. Significantly, this document encapsulates important advances in melanocyte and melanoma research including emerging frontiers in melanoma immunotherapy, medical and surgical oncology, dermatology, vitiligo, albinism, genomics and systems biology, epidemiology, pigment biophysics and chemistry, and evolution

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Visible light is a better co-inducer of apoptosis for curcumin-treated human melanoma cells than UVA

Curcumin attracts worldwide scientific interest due to its anti-proliferative and apoptosis inducing effects on different tumor cells at concentrations ranging from 10 to 150 µM (3.7–55 µg/ml). Unfortunately, because of a low oral bioavailability, only low and pharmacologically ineffective serum levels are achievable. In this study, an alternative treatment concept consisting of low concentration curcumin (0.2–5 µg/ml) and irradiation with UVA or visible light (VL) has been tested. The experimental results show clearly that this treatment decreases the proliferation and the viability of human melanoma cells while the cell membrane integrity remains intact. We identified the onset of apoptosis characterized by typical markers such as active caspases 8, 9 and 3 as well as DNA fragmentation accompanied by the loss of cell adhesion. The mitochondrial apoptosis signaling pathway is predominant due to an early activation of caspase-9. The present data indicate a higher efficacy of a combination of curcumin and VL than curcumin and UVA. Reduced effects as a result of light absorption by heavily pigmented skin are unlikely if VL is used. These results indicate that a combination of curcumin and light irradiation may be a useful additional therapy in the treatment of malignant disease