The number of deaths linked to skin cancers has malignant melanoma as the main culprit. Early diagnosis helps manage this terrible form of cancer, but the similarity of melanoma to other skin lesions is an obstacle to efective detection. The scientific community is proposing diferent solutions to support the computerized analysis of skin lesions mainly focused on the dichotomous distinction of melanoma from benign lesions. The dysplastic nevi syndrome (DNS) correlates the number of moles present in the human body with an increased risk of melanoma development. Nowadays, the classification task concerning the diferentiation of dysplastic nevi from common ones is still very little explored. In this paper, we explore the possibility of applying multiple instance learning (MIL) approaches to discriminate melanoma from dysplastic nevi and outline the even more complex challenge of discriminate between dysplastic and common nevi. The obtained results confirm that MIL techniques are useful for the automatic detection of skin lesions are promising, and give hope MIL techniques can be useful for solutions aiming at automatic detection of skin lesions.
The World Health Organization certifies that, in 2020, more than 57,000 people died of melanoma
and that there were more than 320,000 new cases. The reported data testify that melanoma afects
the populations of all geographical areas of the world and in particular those of Europe (50.1 % of
total cases) and North America (27.7 % of total cases). Melanoma ranks 5th for age-standardized
(World) incidence and mortality rates in 2020, for both males and females, considering all ages
[
In particular, the used dataset contains dermatoscopic images: this particular type is widely used in Computer Aided Diagnosis (CAD) systems to support the diagnosis.
Considering that higher risk of developing melanoma pertains to individuals with dysplastic
nevi syndrome and/or with family history of melanoma, our research focuses on the application
of DC-SMIL [
The first task results to be dificult for the great similarity of the two types of lesions [
The paper is organized as follows. In the next section we put in evidence that the presence of
dysplastic nevi and common nevi may imply risk of melanoma onset. In Section 3 we introduce
the Multiple Instance Learning approach, focusing on DC-SMIL a new MIL algoritmh that adopt
spherical separation surfaces [
The Syndrome of Dysplastic Nevus (DNS) refers to individuals that present a high number of
both benign moles and dysplastic nevi. Individuals with dysplastic nevi are more likely to
develop melanoma if familiarly conditions exists. In [
Fewer attentions have been given to the discrimination of melanoma from dysplastic nevi
[
Machine Learning has become very important in medical image analysis. In fact, machine learning methods are currently used in the segmentation steps, in which each pixel of an image belongs to a particular tissue and in CAD systems to assign a category label to a whole image.
Multiple Instance Learning scenario is particularly useful when disposing of local annotated labels is expensive, while global labels for whole images, such as the outcome of a diagnosis, are more readily available. MIL is an extension of supervised learning that can train classifiers using weakly labeled data. The goal is therefore to exploit the labels of the weaker bags for training. A MIL problem consists in the classification task of a set of items called bags and of the objects inside them called instances. The substantial diference compared to supervised classification consists in the fact that, in the learning phase, only the labels of the bags are known, and not those of the instances.
The MIL paradigm is particularly well suited to image classification, given that to classify an
image, it is necessary to examine only some sub-regions. With MIL approaches it is therefore
possible to obtain global information from local one. For general considerations on the MIL
paradigm, we refer the reader to surveys [
In [16] we applied MIL-RL algorithm to discriminate melanoma from benign lesion. The results demonstrate the goodness of the proposed approach.
In a data driven way, we have therefore presented a new algorithm named DC-SMIL [
Our aim is to find a sphere (, ) ⊂ R, of center ∈ R and radius ∈ R, separating the two classes of bags. In order to separate the positive bags 1+, . . . , + from the negative ones 1− , . . . , − , a sphere must have a nonempty intersection with each positive bag, while leaving outside all the instances belonging to negative bags.
A pictorial example of spherical separation is presented in Figure 2, where the sphere (, ) separates the negative bags 1− , 2− , and 3− from the positive bags 1+ and 2+. In particular, we remark that while the bags depicted in Figure 2 are spherically separable, they are not separable by any hyper-plane.
Based on the latter remark an optimization model was obtained with the aim to look for a separating sphere, if any, by minimizing a measure of all the classification errors of both the negative and the positive bags, that is where the loss function is defined as
min (,)∈R+1
(, ) {︃ (, ) ≜ 2 + ∑︁ max 0, max {︀ 2 − ‖ − ‖2}︀ =1 ∈−
}︃ {︃ + ∑︁ max 0, min {︀ ‖ − ‖2 − 2}︀ =1 ∈+ }︃ In particular, such loss function accounts for three contributions: • the first term accounts for the volume of the sphere; • the second one accounts for the misclassification error of the negative bags; • the last term accounts for misclassification error of the positive bags.
Hence, the Spherical MIL program (SMIL) follows as the unconstrained optimization problem min (,)∈R+1 (, ) ≜ 2 + ℰ (, ), (1) (2) (3) which combines, by introducing a trade-of parameter > 0, the two objectives of minimizing the radius of the sphere and the classification errors of all the negative and positive bags. Here the radius minimization is aimed at reducing the false positive phenomenon when the calculated sphere is used as a classification tool.
We have performed experiments applying DC-SMIL on various data sets to evaluate the goodness of the proposed technique and to compare the obtained results with those of other MIL methods. In particular, we applied DC-SMIL on a real dermatoscopic dataset ( 2), with the aim of verifying that MIL spherical separation approach may be of interest in classification tasks in which the data to be classified have extreme similarity.
The entire 2 database contains 200 images of melanocytic lesions: 80 common nevi, 80 atypical nevi and 40 melanomas. All images were obtained using 8-bit RGB colors with a resolution of 768 × 560 pixels.
For the classification experiments we considered the images without taking into account the indications resulting from the manual analysis carried out by the specialists.
In [17] the authors demonstrated how, by adopting only color features, satisfactory
classification performances can be obtained using dermatoscopic images. Starting from this assumptions,
we used a 30-dimensional vector for the representation of each sub-regions of each image. For
further details please see [16] and [
In order to appreciate the MIL classification paradigm, we report in the columns MIL-RL, SVM and SVM-RBF the results obtained using MIL-RL algorithm and standard SVM approach [18] with linear and RBF kernels, respectively. The best results in Tables 1 and 2 have been underlined.
From numerical experiments it emerges that, in general, MIL-RL overcomes DC-SMIL and SVM technique (with both linear and RBF kernels) in terms of accuracy and sensitivity. Whenever accuracy is not 100%, low specificity values are a consequence of high sensitivity values.
In medical fields, sensitivity plays a more important role than specificity since it is a measure of the ability to identify un-healthy patients. The F-score values show the good performance of the MIL approach in classifying melanoma from dysplastic nevi against the classic SVM technique.
CPU time (secs)
With regard to the experimental section on the classification of dysplastic nevi against common nevi, the performances of MIL-RL and of SVM tecniques appear totally unsatisfactory. This is obvious because the images that were separated are very similar. MIL-RL registers the worst value of F-score and sensitivity, and overall it is not efective to solve the proposed task.
CPU time (secs)
DC-SMIL 59.38 59.73 59.88 57.61 0.58
10-CV MIL-RL 59.38 31.77 87.06 42.77 1.71
The use of spherical separating surfaces, provided by DC-SMIL algorithm, allows significant improvements in the extremely dificult task of classify dysplastic nevi from common ones.
As shown in [19, 20] better results could be obtained in case of images pre-processing aimed at eliminating the presence of possible noises, such as possible hair. Even the adoption of further useful features extracted from blob is a possibility that would allow to improve the classification performances [21, 29]. Pre-processing steps and the adoption of a more numerous set of features appear to be an obligatory step when considering non-dermatoscopic images [22, 23].
The obtained results show that in the first case MIL-RL is very promising, even in the conditions in which we performed the experiments, i.e. with only color features and without using pre-processing steps.
In the second case, MIL-RL algorithm as well as the SVM in the linear and Kernel RBF version, do not give satisfactory results. The excessive similarity of the lesions is not properly discriminated with approaches aimed at identifying linear separation surfaces. On the other hand DC-SMIL, thanks to the use of spherical separation surfaces, seems to be an interesting proposal for the development of applications in contexts in which positive and negative elements have similar characteristics.
Our proposal based on MIL approaches, among the various proposals of artificial intelligence in this specific domain, constitutes an element of novelty [ 26]. Our goal is to set propose a framework for supporting diagnostics both for specialists and for patient self-diagnosis examination via mobile applications. In this way, modular solution which can be incorporated into integrated diagnostic systems [27, 28] would increase the value of the proposal.
Future research could include the design of more sophisticated segmentation techniques in order to further improve classification results, as well as the application of the proposed method in other medical fields [29, 30] to identify other types of injuries. [16] A. Astorino, et al., Melanoma Detection by Means of Multiple Instance Learning. Interdiscip
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