Skin Lesion Segmentation Ensemble with Diverse Training Strategies

This paper presents a novel strategy to perform skin lesion segmentation from dermoscopic images. We design an effective segmentation pipeline, and explore several pre-training methods to initialize the features extractor, highlighting how different procedures lead the Convolutional Neural Network (CNN) to focus on different features. An encoder-decoder segmentation CNN is employed to take advantage of each pre-trained features extractor. Experimental results reveal how multiple initialization strategies can be exploited, by means of an ensemble method, to obtain state-of-the-art skin lesion segmentation accuracy

Transformer-Based Approach to Melanoma Detection

Melanoma is a malignant cancer type which develops when DNA damage occurs (mainly due to environmental factors such as ultraviolet rays). Often, melanoma results in intense and aggressive cell growth that, if not caught in time, can bring one toward death. Thus, early identification at the initial stage is fundamental to stopping the spread of cancer. In this paper, a ViT-based architecture able to classify melanoma versus non-cancerous lesions is presented. The proposed predictive model is trained and tested on public skin cancer data from the ISIC challenge, and the obtained results are highly promising. Different classifier configurations are considered and analyzed in order to find the most discriminating one. The best one reached an accuracy of 0.948, sensitivity of 0.928, specificity of 0.967, and AUROC of 0.948

Towards equitable deep learning in dermatology: assessing lesion classification fairness across skin tones

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona. Curs: 2022-2023. Tutor: Oliver Díaz i Richard OsualaRecent advances in deep learning skin lesion classifiers rose expectations that these models can be implemented in the clinical routine in the near future. However, before deploying deep learning models in such a sensitive area as healthcare, it is important to ensure their trustworthiness and mitigate any kind of discrimination. This thesis investigates discrimination by skin tone in a light-weight deep learning skin lesion classifier trained on a benchmark dataset of dermatological images and assesses the feasibility of SinGAN-generated synthetic dark skin images to improve predictions on dark skin samples in the absence of dark skin training data. The results suggest that (I) there is discrimination by skin tone, (II) a data shift from apparent light skin samples in training to apparent dark skin samples in deployment deteriorates predictions, and (III) although dark SinGAN-generated samples may improve performance, oversampling of a few dark skin samples appears more feasible. Most importantly, however, a thorough analysis of automated skin tone estimations with the Individual Topology Angle revealed that (IV) these skin tone estimations might measure the darkness of a skin image rather than the darkness of skin in the image and (V) the investigated HAM10000 dataset is less diverse than previous research suggested. This has potentially wide-ranging implications for previous publications about skin tone fairness using this dataset and emphasizes the need for further research on more diverse dermatology datasets with more reliable skin tone labels before wide-spread deployment of skin lesion classifiers

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

Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval

In recent years, many attempts have been dedicated to the creation of automated devices that could assist both expert and beginner dermatologists towards fast and early diagnosis of skin lesions. Tasks such as skin lesion classification and segmentation have been extensively addressed with deep learning algorithms, which in some cases reach a diagnostic accuracy comparable to that of expert physicians. However, the general lack of interpretability and reliability severely hinders the ability of those approaches to actually support dermatologists in the diagnosis process. In this paper a novel skin image retrieval system is presented, which exploits features extracted by Convolutional Neural Networks to gather similar images from a publicly available dataset, in order to assist the diagnosis process of both expert and novice practitioners. In the proposed framework, ResNet-50 is initially trained for the classification of dermoscopic images; then, the feature extraction part is isolated, and an embedding network is built on top of it. The embedding learns an alternative representation, which allows to check image similarity by means of a distance measure. Experimental results reveal that the proposed method is able to select meaningful images, which can effectively boost the classification accuracy of human dermatologists

A deep analysis on high resolution dermoscopic image classification

[EN] Convolutional neural networks (CNNs) have been broadly employed in dermoscopic image analysis, mainly as a result of the large amount of data gathered by the International Skin Imaging Collaboration (ISIC). As in many other medical imaging domains, state-of-the-art methods take advantage of architectures developed for other tasks, frequently assuming full transferability between enormous sets of natural images (e.g. ImageNet) and dermoscopic images, which is not always the case. A comprehensive analysis on the effectiveness of state-of-the-art deep learning techniques when applied to dermoscopic image analysis is provided. To achieve this goal, the authors consider several CNNs architectures and analyse how their performance is affected by the size of the network, image resolution, data augmentation process, amount of available data, and model calibration. Moreover, taking advantage of the analysis performed, a novel ensemble method to further increase the classification accuracy is designed. The proposed solution achieved the third best result in the 2019 official ISIC challenge, with an accuracy of 0.593.Juan Maroñas is supported by grant FPI-UPV, grant agreement No 825,111 DeepHealth Project, and by the Spanish National Ministry of Education through grant RTI2018-098091-B-I00. The research leading to these results has received funding from the European Union through Programa Operativo del Fondo Europeo de Desarrollo Regional (FEDER) from Comunitat Valencia (2014-2020) under project Sistemas de frabricación inteligentes para la indústria 4.0 (grant agreement IDIFEDER/2018/025).Pollastri, F.; Parreño Lara, M.; Maroñas-Molano, J.; Bolelli, F.; Paredes Palacios, R.; Ramos, D.; Grana, C. (2021). A deep analysis on high resolution dermoscopic image classification. IET Computer Vision. 15(7):514-526. https://doi.org/10.1049/cvi2.1204851452615

SkinCAN AI: A deep learning-based skin cancer classification and segmentation pipeline designed along with a generative model

The rarity of Melanoma skin cancer accounts for the dataset collected to be limited and highly skewed, as benign moles can easily mimic the impression of the melanoma-affected area. Such an imbalanced dataset makes training any deep learning classifier network harder by affecting the training stability. We have an intuition that synthesizing such skin lesion medical images could help solve the issue of overfitting in training networks and assist in enforcing the anonymization of actual patients. Despite multiple previous attempts, none of the models were practical for the fast-paced clinical environment. In this thesis, we propose a novel pipeline named SkinCAN AI, inspired by StyleGAN but designed explicitly considering the limitations of the skin lesion dataset and emphasizing the requirement of a faster optimized diagnostic tool that can be easily inferred and integrated into the clinical environment. Our SkinCAN AI model is equipped with its module of adaptive discriminator augmentation that enables limited target data distribution to be learned and artificial data points to be sampled, which further assist the classifier network in learning semantic features. We elucidate the novelty of our SkinCAN AI pipeline by integrating the soft attention module in the classifier network. This module yields an attention mask analyzed by DenseNet201 to focus on learning relevant semantic features from skin lesion images without using any heavy computational burden of artifact removal software. The SkinGAN model achieves an FID score of 0.622 while allowing its synthetic samples to train the DenseNet201 model with an accuracy of 0.9494, AUC of 0.938, specificity of 0.969, and sensitivity of 0.695. We provide evidence in our thesis that our proposed pipelines outperform other state-of-the-art existing networks developed for this task of early diagnosis

Skin disease analysis with limited data in particular Rosacea: a review and recommended framework

Recently, the rapid advancements in Deep Learning and Computer Vision technologies have introduced a new and exciting era in the field of skin disease analysis. However, there are certain challenges in the roadmap towards developing such technologies for real-life applications that must be investigated. This study considers one of the key challenges in data acquisition and computation, viz. data scarcity. Data scarcity is a central problem in acquiring medical images and applying machine learning techniques to train Convolutional Neural Networks for disease diagnosis. The main objective of this study is to explore the possible methods to deal with the data scarcity problem and to improve diagnosis with small datasets. The challenges in data acquisition for a few lamentably neglected skin conditions such as rosacea are an excellent instance to explore the possibilities of improving computer-aided skin disease diagnosis. With data scarcity in mind, the possible techniques explored and discussed include Generative Adversarial Networks, Meta-Learning, Few-Shot classification, and 3D face modelling. Furthermore, the existing studies are discussed based on skin conditions considered, data volume and implementation choices. Some future research directions are recommended