The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available datasets of dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human Against Machine with 10000 training images") dataset. We collected dermatoscopic images from different populations acquired and stored by different modalities. Given this diversity we had to apply different acquisition and cleaning methods and developed semi-automatic workflows utilizing specifically trained neural networks. The final dataset consists of 10015 dermatoscopic images which are released as a training set for academic machine learning purposes and are publicly available through the ISIC archive. This benchmark dataset can be used for machine learning and for comparisons with human experts. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions. More than 50% of lesions have been confirmed by pathology, while the ground truth for the rest of the cases was either follow-up, expert consensus, or confirmation by in-vivo confocal microscopy

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival

Stacked Cross Validation with Deep Features: A Hybrid Method for Skin Cancer Detection

Detection of malignant skin lesions is important for early and accurate diagnosis of skin cancer. In this work, a hybrid method for malignant lesion detection from dermoscopy images is proposed. The method combines the feature extraction process of convolutional neural networks (CNN) with an ensemble learner called stacked cross-validation (CV). The features extracted by three different CNN architectures, namely, ResNet50, Xception, and VGG16 are used for training of four different baseline classifiers, which are support vector machines, k-nearest neighbors, artificial neural networks, and random forests. The stacked outputs of these classifiers are used to train a logistic regression model as a meta-classifier. The performance of the proposed method is compared with the baseline classifiers trained individually as well as AdaBoost classifier, another ensemble learner. Feature extraction with Xception architecture, outperforms all other benchmark models by achieving scores of 0.909, 0.896, 0.886, and 0.917 for accuracy, F1-score, sensitivity, and AUC, respectively

Does a Previous Segmentation Improve the Automatic Detection of Basal Cell Carcinoma Using Deep Neural Networks?

This article belongs to the Special Issue "Image Processing and Analysis for Preclinical and Clinical Applications"Basal Cell Carcinoma (BCC) is the most frequent skin cancer and its increasing incidence is producing a high overload in dermatology services. In this sense, it is convenient to aid physicians in detecting it soon. Thus, in this paper, we propose a tool for the detection of BCC to provide a prioritization in the teledermatology consultation. Firstly, we analyze if a previous segmentation of the lesion improves the ulterior classification of the lesion. Secondly, we analyze three deep neural networks and ensemble architectures to distinguish between BCC and nevus, and BCC and other skin lesions. The best segmentation results are obtained with a SegNet deep neural network. A 98% accuracy for distinguishing BCC from nevus and a 95% accuracy classifying BCC vs. all lesions have been obtained. The proposed algorithm outperforms the winner of the challenge ISIC 2019 in almost all the metrics. Finally, we can conclude that when deep neural networks are used to classify, a previous segmentation of the lesion does not improve the classification results. Likewise, the ensemble of different neural network configurations improves the classification performance compared with individual neural network classifiers. Regarding the segmentation step, supervised deep learning-based methods outperform unsupervised onesMinisterio de Economía y Competitividad DPI2016-81103-RFEDER-US, Junta de Andalucía US-1381640Fondo Social Europeo Iniciativa de Empleo Juvenil EJ3-83-

Towards PACE-CAD Systems

Despite phenomenal advancements in the availability of medical image datasets and the development of modern classification algorithms, Computer-Aided Diagnosis (CAD) has had limited practical exposure in the real-world clinical workflow. This is primarily because of the inherently demanding and sensitive nature of medical diagnosis that can have far-reaching and serious repercussions in case of misdiagnosis. In this work, a paradigm called PACE (Pragmatic, Accurate, Confident, & Explainable) is presented as a set of some of must-have features for any CAD. Diagnosis of glaucoma using Retinal Fundus Images (RFIs) is taken as the primary use case for development of various methods that may enrich an ordinary CAD system with PACE. However, depending on specific requirements for different methods, other application areas in ophthalmology and dermatology have also been explored. Pragmatic CAD systems refer to a solution that can perform reliably in day-to-day clinical setup. In this research two, of possibly many, aspects of a pragmatic CAD are addressed. Firstly, observing that the existing medical image datasets are small and not representative of images taken in the real-world, a large RFI dataset for glaucoma detection is curated and published. Secondly, realising that a salient attribute of a reliable and pragmatic CAD is its ability to perform in a range of clinically relevant scenarios, classification of 622 unique cutaneous diseases in one of the largest publicly available datasets of skin lesions is successfully performed. Accuracy is one of the most essential metrics of any CAD system's performance. Domain knowledge relevant to three types of diseases, namely glaucoma, Diabetic Retinopathy (DR), and skin lesions, is industriously utilised in an attempt to improve the accuracy. For glaucoma, a two-stage framework for automatic Optic Disc (OD) localisation and glaucoma detection is developed, which marked new state-of-the-art for glaucoma detection and OD localisation. To identify DR, a model is proposed that combines coarse-grained classifiers with fine-grained classifiers and grades the disease in four stages with respect to severity. Lastly, different methods of modelling and incorporating metadata are also examined and their effect on a model's classification performance is studied. Confidence in diagnosing a disease is equally important as the diagnosis itself. One of the biggest reasons hampering the successful deployment of CAD in the real-world is that medical diagnosis cannot be readily decided based on an algorithm's output. Therefore, a hybrid CNN architecture is proposed with the convolutional feature extractor trained using point estimates and a dense classifier trained using Bayesian estimates. Evaluation on 13 publicly available datasets shows the superiority of this method in terms of classification accuracy and also provides an estimate of uncertainty for every prediction. Explainability of AI-driven algorithms has become a legal requirement after Europe’s General Data Protection Regulations came into effect. This research presents a framework for easy-to-understand textual explanations of skin lesion diagnosis. The framework is called ExAID (Explainable AI for Dermatology) and relies upon two fundamental modules. The first module uses any deep skin lesion classifier and performs detailed analysis on its latent space to map human-understandable disease-related concepts to the latent representation learnt by the deep model. The second module proposes Concept Localisation Maps, which extend Concept Activation Vectors by locating significant regions corresponding to a learned concept in the latent space of a trained image classifier. This thesis probes many viable solutions to equip a CAD system with PACE. However, it is noted that some of these methods require specific attributes in datasets and, therefore, not all methods may be applied on a single dataset. Regardless, this work anticipates that consolidating PACE into a CAD system can not only increase the confidence of medical practitioners in such tools but also serve as a stepping stone for the further development of AI-driven technologies in healthcare