Asking 'why' in AI: Explainability of intelligent systems - perspectives and challenges

Recent rapid progress in machine learning (ML), particularly so‐called ‘deep learning’, has led to a resurgence in interest in explainability of artificial intelligence (AI) systems, reviving an area of research dating back to the 1970s. The aim of this article is to view current issues concerning ML‐based AI systems from the perspective of classical AI, showing that the fundamental problems are far from new, and arguing that elements of that earlier work offer routes to making progress towards explainable AI today

Skin lesions classification using convolutional neural networks in clinical images

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade UnB Gama, 2018.Lesões de pele são condições que aparecem em um paciente devido a várias razões. Uma delas pode ser por causa de um crescimento anormal no tecido da pele, definido como câncer. Essa doença aflige mais de 14,1 milhões de pacientes e tem sido a causa de mais de 8,2 milhões de mortes no mundo todo. Sendo assim, uma solução capaz de ajudar no diagnóstico precoce pode salvar vidas e diminuir custos de tratamento. Visto isso, é proposto a construção de um modelo de classificação para 12 lesões, sendo dessas 4 malignas, incluindo Melanoma Maligno e Carcinoma Basocelular. Além disso, neste trabalho é utilizado uma arquitetura ResNet-152 pré-treinada, que então foi aprimorada com 88,090 imagens aumentadas, utilizando diferentes transformações. As predições foram então analizadas com o método GradCAM para gerar explicações visuais, que foram condizentes com conhecimentos prévios e boas práticas para explicações. Finalmente, a rede foi testada com 956 imagens e alcançou a métrica de área abaixo da curva (AUC) de 0.96 para Melanoma e 0.91 para Carcinoma Basocelular, comparaveis aos resultados de estado da arte.Skin lesions are conditions that appear on a patient due to many different reasons. One of these can be because of an abnormal growth in skin tissue, defined as cancer. This disease plagues more than 14.1 million patients and had been the cause of more than 8.2 million deaths, worldwide. Furthermore, a solution capable of aiding early diagnosis may save lives and cut costs in treatment. Therefore, this work proposes the construction of a classification model for 12 lesions, being 4 of these malignant, including Malignant Melanoma and Basal Cell Carcinoma. Furthermore, we use a pre-trained ResNet-152 architecture, which then was trained over 88,090 augmented images, using different transformations. The predictions were then analyzed with GradCAM method, to generate visual explanations, which were consistent with a prior belief and general good practices for explanations. Finally, the network was tested with 956 original images and achieve an area under the curve (AUC) metric of 0.96 for Melanoma and 0.91 for Basal Cell Carcinoma, that is comparable to state-of-the-art results

Classification of skin tumours through the analysis of unconstrained images

Skin cancer is the most frequent malignant neoplasm for Caucasian individuals. According to the Skin Cancer Foundation, the incidence of melanoma, the most malignant of skin tumours, and resultant mortality, have increased exponentially during the past 30 years, and continues to grow. [1]. Although often intractable in advanced stages, skin cancer in general and melanoma in particular, if detected in an early stage, can achieve cure ratios of over 95% [1,55]. Early screening of the lesions is, therefore, crucial, if a cure is to be achieved. Most skin lesions classification systems rely on a human expert supported dermatoscopy, which is an enhanced and zoomed photograph of the lesion zone. Nevertheless and although contrary claims exist, as far as is known by the author, classification results are currently rather inaccurate and need to be verified through a laboratory analysis of a piece of the lesion’s tissue. The aim of this research was to design and implement a system that was able to automatically classify skin spots as inoffensive or dangerous, with a small margin of error; if possible, with higher accuracy than the results achieved normally by a human expert and certainly better than any existing automatic system. The system described in this thesis meets these criteria. It is able to capture an unconstrained image of the affected skin area and extract a set of relevant features that may lead to, and be representative of, the four main classification characteristics of skin lesions: Asymmetry; Border; Colour; and Diameter. These relevant features are then evaluated either through a Bayesian statistical process - both a simple k-Nearest Neighbour as well as a Fuzzy k-Nearest Neighbour classifier - a Support Vector Machine and an Artificial Neural Network in order to classify the skin spot as either being a Melanoma or not. The characteristics selected and used through all this work are, to the author’s knowledge, combined in an innovative manner. Rather than simply selecting absolute values from the images characteristics, those numbers were combined into ratios, providing a much greater independence from environment conditions during the process of image capture. Along this work, image gathering became one of the most challenging activities. In fact several of the initially potential sources failed and so, the author had to use all the pictures he could find, namely on the Internet. This limited the test set to 136 images, only. Nevertheless, the process results were excellent. The algorithms developed were implemented into a fully working system which was extensively tested. It gives a correct classification of between 76% and 92% – depending on the percentage of pictures used to train the system. In particular, the system gave no false negatives. This is crucial, since a system which gave false negatives may deter a patient from seeking further treatment with a disastrous outcome. These results are achieved by detecting precise edges for every lesion image, extracting features considered relevant according to the giving different weights to the various extracted features and submitting these values to six classification algorithms – k-Nearest Neighbour, Fuzzy k-Nearest Neighbour, Naïve Bayes, Tree Augmented Naïve Bayes, Support Vector Machine and Multilayer Perceptron - in order to determine the most reliable combined process. Training was carried out in a supervised way – all the lesions were previously classified by an expert on the field before being subject to the scrutiny of the system. The author is convinced that the work presented on this PhD thesis is a valid contribution to the field of skin cancer diagnostics. Albeit its scope is limited – one lesion per image – the results achieved by this arrangement of segmentation, feature extraction and classification algorithms showed this is the right path to achieving a reliable early screening system. If and when, to all these data, values for age, gender and evolution might be used as classification features, the results will, no doubt, become even more accurate, allowing for an improvement in the survival rates of skin cancer patients