Partial order label decomposition approaches for melanoma diagnosis

Melanoma is a type of cancer that develops from the pigment-containing cells known as melanocytes. Usually occurring on the skin, early detection and diagnosis is strongly related to survival rates. Melanoma recognition is a challenging task that nowadays is performed by well trained dermatologists who may produce varying diagnosis due to the task complexity. This motivates the development of automated diagnosis tools, in spite of the inherent difficulties (intra-class variation, visual similarity between melanoma and non-melanoma lesions, among others). In the present work, we propose a system combining image analysis and machine learning to detect melanoma presence and severity. The severity is assessed in terms of melanoma thickness, which is measured by the Breslow index. Previous works mainly focus on the binary problem of detecting the presence of the melanoma. However, the system proposed in this paper goes a step further by also considering the stage of the lesion in the classification task. To do so, we extract 100 features that consider the shape, colour, pigment network and texture of the benign and malignant lesions. The problem is tackled as a five-class classification problem, where the first class represents benign lesions, and the remaining four classes represent the different stages of the melanoma (via the Breslow index). Based on the problem definition, we identify the learning setting as a partial order problem, in which the patterns belonging to the different melanoma stages present an order relationship, but where there is no order arrangement with respect to the benign lesions. Under this assumption about the class topology, we design several proposals to exploit this structure and improve data preprocessing. In this sense, we experimentally demonstrate that those proposals exploiting the partial order assumption achieve better performance than 12 baseline nominal and ordinal classifiers (including a deep learning model) which do not consider this partial order. To deal with class imbalance, we additionally propose specific over-sampling techniques that consider the structure of the problem for the creation of synthetic patterns. The experimental study is carried out with clinician-curated images from the Interactive Atlas of Dermoscopy, which eases reproducibility of experiments. Concerning the results obtained, in spite of having augmented the complexity of the classification problem with more classes, the performance of our proposals in the binary problem is similar to the one reported in the literature

Partial order label decomposition approaches for melanoma diagnosis

Melanoma is a type of cancer that develops from the pigment-containing cells known as melanocytes. Usually occurring on the skin, early detection and diagnosis is strongly related to survival rates. Melanoma recognition is a challenging task that nowadays is performed by well trained dermatologists who may produce varying diagnosis due to the task complexity. This motivates the development of automated diagnosis tools, in spite of the inherent difficulties (intra-class variation, visual similarity between melanoma and non-melanoma lesions, among others). In the present work, we propose a system combining image analysis and machine learning to detect melanoma presence and severity. The severity is assessed in terms of melanoma thickness, which is measured by the Breslow index. Previous works mainly focus on the binary problem of detecting the presence of the melanoma. However, the system proposed in this paper goes a step further by also considering the stage of the lesion in the classification task. To do so, we extract 100 features that consider the shape, colour, pigment network and texture of the benign and malignant lesions. The problem is tackled as a five-class classification problem, where the first class represents benign lesions, and the remaining four classes represent the different stages of the melanoma (via the Breslow index). Based on the problem definition, we identify the learning setting as a partial order problem, in which the patterns belonging to the different melanoma stages present an order relationship, but where there is no order arrangement with respect to the benign lesions. Under this assumption about the class topology, we design several proposals to exploit this structure and improve data preprocessing. In this sense, we experimentally demonstrate that those proposals exploiting the partial order assumption achieve better performance than 12 baseline nominal and ordinal classifiers (including a deep learning model) which do not consider this partial order. To deal with class imbalance, we additionally propose specific over-sampling techniques that consider the structure of the problem for the creation of synthetic patterns. The experimental study is carried out with clinician-curated images from the Interactive Atlas of Dermoscopy, which eases reproducibility of experiments. Concerning the results obtained, in spite of having augmented the complexity of the classification problem with more classes, the performance of our proposals in the binary problem is similar to the one reported in the literature

Building a semantically annotated corpus of clinical texts

In this paper, we describe the construction of a semantically annotated corpus of clinical texts for use in the development and evaluation of systems for automatically extracting clinically significant information from the textual component of patient records. The paper details the sampling of textual material from a collection of 20,000 cancer patient records, the development of a semantic annotation scheme, the annotation methodology, the distribution of annotations in the final corpus, and the use of the corpus for development of an adaptive information extraction system. The resulting corpus is the most richly semantically annotated resource for clinical text processing built to date, whose value has been demonstrated through its use in developing an effective information extraction system. The detailed presentation of our corpus construction and annotation methodology will be of value to others seeking to build high-quality semantically annotated corpora in biomedical domains

Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis

Many subproblems in automated skin lesion diagnosis (ASLD) canbe unified under a single generalization of assigning a label, from an predefinedset, to each pixel in an image. We first formalize this generalizationand then present two probabilistic models capable of solving it. The firstmodel is based on independent pixel labeling using maximum a-posteriori(MAP) estimation. The second model is based on conditional randomfields (CRFs), where dependencies between pixels are defined using agraph structure. Furthermore, we demonstrate how supervised learningand an appropriate training set can be used to automatically determineall model parameters. We evaluate both models\u27 ability to segment achallenging dataset consisting of 116 images and compare our results to5 previously published methods

Conditional Random Fields and Supervised Learning in Automated Skin Lesion Diagnosis

Many subproblems in automated skin lesion diagnosis (ASLD) can be unified under a single generalization of assigning a label, from an predefined set, to each pixel in an image. We first formalize this generalization and then present two probabilistic models capable of solving it. The first model is based on independent pixel labeling using maximum a-posteriori (MAP) estimation. The second model is based on conditional random fields (CRFs), where dependencies between pixels are defined using a graph structure. Furthermore, we demonstrate how supervised learning and an appropriate training set can be used to automatically determine all model parameters. We evaluate both models' ability to segment a challenging dataset consisting of 116 images and compare our results to 5 previously published methods

Graph Clustering, Variational Image Segmentation Methods and Hough Transform Scale Detection for Object Measurement in Images

© 2016, Springer Science+Business Media New York. We consider the problem of scale detection in images where a region of interest is present together with a measurement tool (e.g. a ruler). For the segmentation part, we focus on the graph-based method presented in Bertozzi and Flenner (Multiscale Model Simul 10(3):1090–1118, 2012) which reinterprets classical continuous Ginzburg–Landau minimisation models in a totally discrete framework. To overcome the numerical difficulties due to the large size of the images considered, we use matrix completion and splitting techniques. The scale on the measurement tool is detected via a Hough transform-based algorithm. The method is then applied to some measurement tasks arising in real-world applications such as zoology, medicine and archaeology

An ordinal CNN approach for the assessment of neurological damage in Parkinson’s disease patients

3D image scans are an assessment tool for neurological damage in Parkinson’s disease (PD) patients. This diagnosis process can be automatized to help medical staff through Decision Support Systems (DSSs), and Convolutional Neural Networks (CNNs) are good candidates, because they are effective when applied to spatial data. This paper proposes a 3D CNN ordinal model for assessing the level or neurological damage in PD patients. Given that CNNs need large datasets to achieve acceptable performance, a data augmentation method is adapted to work with spatial data. We consider the Ordinal Graph-based Oversampling via Shortest Paths (OGO-SP) method, which applies a gamma probability distribution for inter-class data generation. A modification of OGO-SP is proposed, the OGO-SP- algorithm, which applies the beta distribution for generating synthetic samples in the inter-class region, a better suited distribution when compared to gamma. The evaluation of the different methods is based on a novel 3D image dataset provided by the Hospital Universitario ‘Reina Sofía’ (Córdoba, Spain). We show how the ordinal methodology improves the performance with respect to the nominal one, and how OGO-SP- yields better performance than OGO-SP

Diagnóstico automático de melanoma mediante técnicas modernas de aprendizaje automático

The incidence and mortality rates of skin cancer remain a huge concern in many countries. According to the latest statistics about melanoma skin cancer, only in the Unites States, 7,650 deaths are expected in 2022, which represents 800 and 470 more deaths than 2020 and 2021, respectively. In 2022, melanoma is ranked as the fifth cause of new cases of cancer, with a total of 99,780 people. This illness is mainly diagnosed with a visual inspection of the skin, then, if doubts remain, a dermoscopic analysis is performed. The development of e_ective non-invasive diagnostic tools for the early stages of the illness should increase quality of life, and decrease the required economic resources. The early diagnosis of skin lesions remains a tough task even for expert dermatologists because of the complexity, variability, dubiousness of the symptoms, and similarities between the different categories among skin lesions. To achieve this goal, previous works have shown that early diagnosis from skin images can benefit greatly from using computational methods. Several studies have applied handcrafted-based methods on high quality dermoscopic and histological images, and on top of that, machine learning techniques, such as the k-nearest neighbors approach, support vector machines and random forest. However, one must bear in mind that although the previous extraction of handcrafted features incorporates an important knowledge base into the analysis, the quality of the extracted descriptors relies heavily on the contribution of experts. Lesion segmentation is also performed manually. The above procedures have a common issue: they are time-consuming manual processes prone to errors. Furthermore, an explicit definition of an intuitive and interpretable feature is hardly achievable, since it depends on pixel intensity space and, therefore, they are not invariant regarding the differences in the input images. On the other hand, the use of mobile devices has sharply increased, which offers an almost unlimited source of data. In the past few years, more and more attention has been paid to designing deep learning models for diagnosing melanoma, more specifically Convolutional Neural Networks. This type of model is able to extract and learn high-level features from raw images and/or other data without the intervention of experts. Several studies showed that deep learning models can overcome handcrafted-based methods, and even match the predictive performance of dermatologists. The International Skin Imaging Collaboration encourages the development of methods for digital skin imaging. Every year since 2016 to 2019, a challenge and a conference have been organized, in which more than 185 teams have participated. However, convolutional models present several issues for skin diagnosis. These models can fit on a wide diversity of non-linear data points, being prone to overfitting on datasets with small numbers of training examples per class and, therefore, attaining a poor generalization capacity. On the other hand, this type of model is sensitive to some characteristics in data, such as large inter-class similarities and intra-class variances, variations in viewpoints, changes in lighting conditions, occlusions, and background clutter, which can be mostly found in non-dermoscopic images. These issues represent challenges for the application of automatic diagnosis techniques in the early phases of the illness. As a consequence of the above, the aim of this Ph.D. thesis is to make significant contributions to the automatic diagnosis of melanoma. The proposals aim to avoid overfitting and improve the generalization capacity of deep models, as well as to achieve a more stable learning and better convergence. Bear in mind that research into deep learning commonly requires an overwhelming processing power in order to train complex architectures. For example, when developing NASNet architecture, researchers used 500 x NVidia P100s - each graphic unit cost from $5,899 to$7,374, which represents a total of $2,949,500.00 -$3,687,000.00. Unfortunately, the majority of research groups do not have access to such resources, including ours. In this Ph.D. thesis, the use of several techniques has been explored. First, an extensive experimental study was carried out, which included state-of-the-art models and methods to further increase the performance. Well-known techniques were applied, such as data augmentation and transfer learning. Data augmentation is performed in order to balance out the number of instances per category and act as a regularizer in preventing overfitting in neural networks. On the other hand, transfer learning uses weights of a pre-trained model from another task, as the initial condition for the learning of the target network. Results demonstrate that the automatic diagnosis of melanoma is a complex task. However, different techniques are able to mitigate such issues in some degree. Finally, suggestions are given about how to train convolutional models for melanoma diagnosis and future interesting research lines were presented. Next, the discovery of ensemble-based architectures is tackled by using genetic algorithms. The proposal is able to stabilize the training process. This is made possible by finding sub-optimal combinations of abstract features from the ensemble, which are used to train a convolutional block. Then, several predictive blocks are trained at the same time, and the final diagnosis is achieved by combining all individual predictions. We empirically investigate the benefits of the proposal, which shows better convergence, mitigates the overfitting of the model, and improves the generalization performance. On top of that, the proposed model is available online and can be consulted by experts. The next proposal is focused on designing an advanced architecture capable of fusing classical convolutional blocks and a novel model known as Dynamic Routing Between Capsules. This approach addresses the limitations of convolutional blocks by using a set of neurons instead of an individual neuron in order to represent objects. An implicit description of the objects is learned by each capsule, such as position, size, texture, deformation, and orientation. In addition, a hyper-tuning of the main parameters is carried out in order to ensure e_ective learning under limited training data. An extensive experimental study was conducted where the fusion of both methods outperformed six state-of-the-art models. On the other hand, a robust method for melanoma diagnosis, which is inspired on residual connections and Generative Adversarial Networks, is proposed. The architecture is able to produce plausible photorealistic synthetic 512 x 512 skin images, even with small dermoscopic and non-dermoscopic skin image datasets as problema domains. In this manner, the lack of data, the imbalance problems, and the overfitting issues are tackled. Finally, several convolutional modes are extensively trained and evaluated by using the synthetic images, illustrating its effectiveness in the diagnosis of melanoma. In addition, a framework, which is inspired on Active Learning, is proposed. The batch-based query strategy setting proposed in this work enables a more faster training process by learning about the complexity of the data. Such complexities allow us to adjust the training process after each epoch, which leads the model to achieve better performance in a lower number of iterations compared to random mini-batch sampling. Then, the training method is assessed by analyzing both the informativeness value of each image and the predictive performance of the models. An extensive experimental study is conducted, where models trained with the proposal attain significantly better results than the baseline models. The findings suggest that there is still space for improvement in the diagnosis of skin lesions. Structured laboratory data, unstructured narrative data, and in some cases, audio or observational data, are given by radiologists as key points during the interpretation of the prediction. This is particularly true in the diagnosis of melanoma, where substantial clinical context is often essential. For example, symptoms like itches and several shots of a skin lesion during a period of time proving that the lesion is growing, are very likely to suggest cancer. The use of different types of input data could help to improve the performance of medical predictive models. In this regard, a _rst evolutionary algorithm aimed at exploring multimodal multiclass data has been proposed, which surpassed a single-input model. Furthermore, the predictive features extracted by primary capsules could be used to train other models, such as Support Vector Machine

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