Melanoma Detection by Means of Multiple Instance Learning

2.5D deep learning based on multi-parameter MRI to differentiate primary lung cancer pathological subtypes in patients with brain metastases

BACKGROUND

Brain metastases (BMs) represents a severe neurological complication stemming from cancers originating from various sources. It is a highly challenging clinical task to accurately distinguish the pathological subtypes of brain metastatic tumors from lung cancer (LC).The utility of 2.5-dimensional (2.5D) deep learning (DL) in distinguishing pathological subtypes of LC with BMs is yet to be determined.

METHODS

A total of 250 patients were included in this retrospective study, divided in a 7:3 ratio into training set (N=175) and testing set (N=75). We devised a method to assemble a series of two-dimensional (2D) images by extracting adjacent slices from a central slice in both superior-inferior and anterior-posterior directions to form a 2.5D dataset. Multi-Instance learning (MIL) is a weakly supervised learning method that organizes training instances into "bags" and provides labels for entire bags, with the purpose of learning a classifier based on the labeled positive and negative bags to predict the corresponding class for an unknown bag. Therefore, we employed MIL to construct a comprehensive 2.5D feature set. Then we used the single-slice as input for constructing the 2D model. DL features were extracted from these slices using the pre-trained ResNet101. All feature sets were inputted into the support vector machine (SVM) for evaluation. The diagnostic performance of the classification models were evaluated using five-fold cross-validation, with accuracy and area under the curve (AUC) metrics calculated for analysis.

RESULTS

The optimal performance was obtained using the 2.5D DL model, which achieved the micro-AUC of 0.868 (95% confidence interval [CI], 0.817-0.919) and accuracy of 0.836 in the test cohort. The 2D model achieved the micro-AUC of 0.836 (95 % CI, 0.778-0.894) and accuracy of 0.827 in the test cohort.

CONCLUSIONS

The proposed 2.5D DL model is feasible and effective in identifying pathological subtypes of BMs from lung cancer.

Annotation protocol and crowdsourcing multiple instance learning classification of skin histological images: The CR-AI4SkIN dataset

Digital Pathology (DP) has experienced a significant growth in recent years and has become an essential tool for diagnosing and prognosis of tumors. The availability of Whole Slide Images (WSIs) and the implementation of Deep Learning (DL) algorithms have paved the way for the appearance of Artificial Intelligence (AI) systems that support the diagnosis process. These systems require extensive and varied data for their training to be successful. However, creating labeled datasets in histopathology is laborious and time-consuming. We have developed a crowdsourcing-multiple instance labeling/learning protocol that is applied to the creation and use of the CR-AI4SkIN dataset.2 CR-AI4SkIN contains 271 WSIs of 7 Cutaneous Spindle Cell (CSC) neoplasms with expert and non-expert labels at region and WSI levels. It is the first dataset of these types of neoplasms made available. The regions selected by the experts are used to learn an automatic extractor of Regions of Interest (ROIs) from WSIs. To produce the embedding of each WSI, the representations of patches within the ROIs are obtained using a contrastive learning method, and then combined. Finally, they are fed to a Gaussian process-based crowdsourcing classifier, which utilizes the noisy non-expert WSI labels. We validate our crowdsourcing-multiple instance learning method in the CR-AI4SkIN dataset, addressing a binary classification problem (malign vs. benign). The proposed method obtains an F1 score of 0.7911 on the test set, outperforming three widely used aggregation methods for crowdsourcing tasks. Furthermore, our crowdsourcing method also outperforms the supervised model with expert labels on the test set (F1-score = 0.6035). The promising results support the proposed crowdsourcing multiple instance learning annotation protocol. It also validates the automatic extraction of interest regions and the use of contrastive embedding and Gaussian process classification to perform crowdsourcing classification tasks.

An optimal method for melanoma detection from dermoscopy images using reinforcement learning and support vector machine optimized by enhanced fish migration optimization algorithm

Early detection of melanoma is crucial in preventing death from this fatal skin cancer. Therefore, it would be valuable to develop a method that facilitates this process. The diagnosis of melanoma typically involves an invasive form of testing called a biopsy, as well as non-invasive intelligent approaches to diagnosis. In the present study a recent research, a novel approach has been developed for the optimal detection of melanoma cancer. The method uses reinforcement learning for segmenting the skin regions, followed by the extraction and selection of useful features using the Enhanced Fish Migration Optimizer (EFMO) algorithm. The outcomes get categorized on the basis of an optimized SVM on the basis of the EFMO algorithm. The recommended approach has been certified by applying it to the SIIM-ISIC dataset of Melanoma and comparing it with 12 other approaches. Simulations illustrated that the proposed method delivered the finest values compared to the others.

Labeling confidence for uncertainty-aware histology image classification

Deep learning-based models applied to digital pathology require large, curated datasets with high-quality (HQ) annotations to perform correctly. In many cases, recruiting expert pathologists to annotate large databases is not feasible, and it is necessary to collect additional labeled data with varying label qualities, e.g., pathologists-in-training (henceforth, non-expert annotators). Learning from datasets with noisy labels is more challenging in medical applications since medical imaging datasets tend to have instance-dependent noise and suffer from high inter/intra-observer variability. In this paper, we design an uncertainty-driven labeling strategy with which we generate soft labels from 10 non-expert annotators for multi-class skin cancer classification. Based on this soft annotation, we propose an uncertainty estimation-based framework to handle these noisy labels. This framework is based on a novel formulation using a dual-branch min-max entropy calibration to penalize inexact labels during the training. Comprehensive experiments demonstrate the promising performance of our labeling strategy. Results show a consistent improvement by using soft labels with standard cross-entropy loss during training (∼4.0% F1-score) and increases when calibrating the model with the proposed min-max entropy calibration (∼6.6% F1-score). These improvements are produced at negligible cost, both in terms of annotation and calculation.

Skin cancer diagnosis based on deep transfer learning and sparrow search algorithm

Skin cancer affects the lives of millions of people every year, as it is considered the most popular form of cancer. In the USA alone, approximately three and a half million people are diagnosed with skin cancer annually. The survival rate diminishes steeply as the skin cancer progresses. Despite this, it is an expensive and difficult procedure to discover this cancer type in the early stages. In this study, a threshold-based automatic approach for skin cancer detection, classification, and segmentation utilizing a meta-heuristic optimizer named sparrow search algorithm (SpaSA) is proposed. Five U-Net models (i.e., U-Net, U-Net++, Attention U-Net, V-net, and Swin U-Net) with different configurations are utilized to perform the segmentation process. Besides this, the meta-heuristic SpaSA optimizer is used to perform the optimization of the hyperparameters using eight pre-trained CNN models (i.e., VGG16, VGG19, MobileNet, MobileNetV2, MobileNetV3Large, MobileNetV3Small, NASNetMobile, and NASNetLarge). The dataset is gathered from five public sources in which two types of datasets are generated (i.e., 2-classes and 10-classes). For the segmentation, concerning the “skin cancer segmentation and classification” dataset, the best reported scores by U-Net++ with DenseNet201 as a backbone architecture are 0.104, $$94.16\%$$ 94.16 % , $$91.39\%$$ 91.39 % , $$99.03\%$$ 99.03 % , $$96.08\%$$ 96.08 % , $$96.41\%$$ 96.41 % , $$77.19\%$$ 77.19 % , $$75.47\%$$ 75.47 % in terms of loss, accuracy, F1-score, AUC, IoU, dice, hinge, and squared hinge, respectively, while for the “PH2” dataset, the best reported scores by the Attention U-Net with DenseNet201 as backbone architecture are 0.137, $$94.75\%$$ 94.75 % , $$92.65\%$$ 92.65 % , $$92.56\%$$ 92.56 % , $$92.74\%$$ 92.74 % , $$96.20\%$$ 96.20 % , $$86.30\%$$ 86.30 % , $$92.65\%$$ 92.65 % , $$69.28\%$$ 69.28 % , and $$68.04\%$$ 68.04 % in terms of loss, accuracy, F1-score, precision, sensitivity, specificity, IoU, dice, hinge, and squared hinge, respectively. For the “ISIC 2019 and 2020 Melanoma” dataset, the best reported overall accuracy from the applied CNN experiments is $$98.27\%$$ 98.27 % by the MobileNet pre-trained model. Similarly, for the “Melanoma Classification (HAM10K)” dataset, the best reported overall accuracy from the applied CNN experiments is $$98.83\%$$ 98.83 % by the MobileNet pre-trained model. For the “skin diseases image” dataset, the best reported overall accuracy from the applied CNN experiments is $$85.87\%$$ 85.87 % by the MobileNetV2 pre-trained model. After computing the results, the suggested approach is compared with 13 related studies.

Deep Learning-Based Fully Automated Diagnosis of Melanocytic Lesions by Using Whole Slide Images

Background Erroneous diagnoses of melanocytic lesions (benign, atypical, and malignant types) result in inappropriate surgical treatment plans.Objective To propose a deep learning (DL)-based fully automated diagnostic method using whole slide images (WSIs) for melanocytic lesions.Methods The method consisted of patch prediction using a DL model and patient diagnosis using an aggregation module. The method was developed with 745 WSIs, and evaluated using internal and external testing sets comprising 182 WSIs and 54 WSIs, respectively. The results were compared with those of the classification by one junior and two senior pathologists. Furthermore, we compared the performance of the three pathologists in the classification of melanocytic lesions with and without the assistance of our method.Results The method achieved an accuracy of 0.963 and 0.930 on the internal and external testing sets, respectively, which was significantly higher than that of the junior pathologist (0.419 and 0.535). With assistance from the method, all three pathologists achieved higher accuracy on the internal and external testing sets; the accuracy of the junior pathologist increased by 39.0% and 30.2%, respectively (p < 0.05).Conclusion This generalizable method can accurately classify melanocytic lesions and effectively improve the diagnostic accuracy of pathologists.

A heuristic approach for multiple instance learning by linear separation

We present a fast heuristic approach for solving a binary multiple instance learning (MIL) problem, which consists in discriminating between two kinds of item sets: the sets are called bags and the items inside them are called instances. Assuming that only two classes of instances are allowed, a common standard hypothesis states that a bag is positive if it contains at least a positive instance and it is negative when all its instances are negative. Our approach constructs a MIL separating hyperplane by preliminary fixing the normal and reducing the learning phase to a univariate nonsmooth optimization problem, which can be quickly solved by simply exploring the kink points. Numerical results are presented on a set of test problems drawn from the literature.

An attention-based weakly supervised framework for spitzoid melanocytic lesion diagnosis in whole slide images

Melanoma is an aggressive neoplasm responsible for the majority of deaths from skin cancer. Specifically, spitzoid melanocytic tumors are one of the most challenging melanocytic lesions due to their ambiguous morphological features. The gold standard for its diagnosis and prognosis is the analysis of skin biopsies. In this process, dermatopathologists visualize skin histology slides under a microscope, in a highly time-consuming and subjective task. In the last years, computer-aided diagnosis (CAD) systems have emerged as a promising tool that could support pathologists in daily clinical practice. Nevertheless, no automatic CAD systems have yet been proposed for the analysis of spitzoid lesions. Regarding common melanoma, no system allows both the selection of the tumor region and the prediction of the benign or malignant form in the diagnosis. Motivated by this, we propose a novel end-to-end weakly supervised deep learning model, based on inductive transfer learning with an improved convolutional neural network (CNN) to refine the embedding features of the latent space. The framework is composed of a source model in charge of finding the tumor patch-level patterns, and a target model focuses on the specific diagnosis of a biopsy. The latter retrains the backbone of the source model through a multiple instance learning workflow to obtain the biopsy-level scoring. To evaluate the performance of the proposed methods, we performed extensive experiments on a private skin database with spitzoid lesions. Test results achieved an accuracy of 0.9231 and 0.80 for the source and the target models, respectively. In addition, the heat map findings are directly in line with the clinicians' medical decision and even highlight, in some cases, patterns of interest that were overlooked by the pathologist.

A Semiproximal Support Vector Machine Approach for Binary Multiple Instance Learning

We face a binary multiple instance learning (MIL) problem, whose objective is to discriminate between two kinds of point sets: positive and negative. In the MIL terminology, such sets are called bags, and the points inside each bag are called instances. Considering the case with two classes of instances (positive and negative) and inspired by a well-established instance-space support vector machine (SVM) model, we propose to extend to MIL classification the proximal SVM (PSVM) technique that has revealed very effective for supervised learning, especially in terms of computational time. In particular, our approach is based on a new instance-space model that exploits the benefits coming from both SVM (better accuracy) and PSVM (computational efficiency) paradigms. Starting from the standard MIL assumption, such a model is aimed at generating a hyperplane placed in the middle between two parallel hyperplanes: the first one is a proximal hyperplane that clusters the instances of the positive bags, while the second one constitutes a supporting hyperplane for the instances of the negative bags. Numerical results are presented on a set of MIL test data sets drawn from the literature.

Skin Cancer Detection Using Kernel Fuzzy C-Means and Improved Neural Network Optimization Algorithm

Early diagnosis of malignant skin cancer from images is a significant part of the cancer treatment process. One of the principal purposes of this research is to propose a pipeline methodology for an optimum computer-aided diagnosis of skin cancers. The method contains four main stages. The first stage is to perform a preprocessing based on noise reduction and contrast enhancement. The second stage is to segment the region of interest (ROI). This study uses kernel fuzzy C-means for ROI segmentation. Then, some features from the ROI are extracted, and then, a feature selection is used for selecting the best ones. The selected features are then injected into a support vector machine (SVM) for final identification. One important part of the contribution in this study is to propose a developed version of a new metaheuristic, named neural network optimization algorithm, to optimize both parts of feature selection and SVM classifier. Comparison results of the method with 5 state-of-the-art methods showed the approach's higher superiority toward the others.

A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers

Background and Objective: Primary lung cancer is a lethal and rapidly-developing cancer type and is one of the most leading causes of cancer deaths. Materials and Methods: Statistical methods such as Cox regression are usually used to detect the prognosis factors of a disease. This study investigated survival prediction using machine learning algorithms. The clinical data of 28,458 patients with primary lung cancers were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Results: This study indicated that the survival rate of women with primary lung cancer was often higher than that of men (p < 0.001). Seven popular machine learning algorithms were utilized to evaluate one-year, three-year, and five-year survival prediction The two classifiers extreme gradient boosting (XGB) and logistic regression (LR) achieved the best prediction accuracies. The importance variable of the trained XGB models suggested that surgical removal (feature “Surgery”) made the largest contribution to the one-year survival prediction models, while the metastatic status (feature “N” stage) of the regional lymph nodes was the most important contributor to three-year and five-year survival prediction. The female patients’ three-year prognosis model achieved a prediction accuracy of 0.8297 on the independent future samples, while the male model only achieved the accuracy 0.7329. Conclusions: This data suggested that male patients may have more complicated factors in lung cancer than females, and it is necessary to develop gender-specific diagnosis and prognosis models.

Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection

Malignant melanoma is the deadliest form of skin cancer and, in recent years, is rapidly growing in terms of the incidence worldwide rate. The most effective approach to targeted treatment is early diagnosis. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis and representation. They optimize the features design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopted pretrained deep convolutional neural networks architectures for the image representation with purpose to predict skin lesion melanoma. Firstly, we applied a transfer learning approach to extract image features. Secondly, we adopted the transferred learning features inside an ensemble classification context. Specifically, the framework trains individual classifiers on balanced subspaces and combines the provided predictions through statistical measures. Experimental phase on datasets of skin lesion images is performed and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors.