=Paper=
{{Paper
|id=Vol-2646/03-paper
|storemode=property
|title=Automatic Detection of Dysplastic Nevi: A Multiple Instance Learning Solution
|pdfUrl=https://ceur-ws.org/Vol-2646/03-paper.pdf
|volume=Vol-2646
|authors=Eugenio Vocaturo,Ester Zumpano
|dblpUrl=https://dblp.org/rec/conf/sebd/VocaturoZ20
}}
==Automatic Detection of Dysplastic Nevi: A Multiple Instance Learning Solution==
https://ceur-ws.org/Vol-2646/03-paper.pdf
Automatic Detection of Dysplastic Nevi: a
Multiple Instance Learning Solution.
(Discussion Paper)
Eugenio Vocaturo1,2[0000−0001−7457−7118] and
Ester Zumpano1[0000−0003−1129−3737]
1
DIMES - Department of Informatics, Modelling, Electronic and System
Engineering University of Calabria, Rende, Italy
{e.vocaturo, e.zumpano}@dimes.unical.it
2
CNR-NANOTEC National Research Council, Rende, Italy
eugenio.vocaturo@cnr.it
Abstract. Malignant melanoma is responsible for the highest number
of deaths related to skin lesions. The similarities of melanoma with other
skin lesions, such as dysplastic nevi, constitute a pitfall for computerized
detection. The proposed algorithms and methods have had as main fo-
cus the dichotomous distinction of melanoma from benign lesions and
they rarely focused on the case of melanoma against dysplastic nevi.
Currently, there is a debate about dysplastic nevi syndrome, or rather
about the number of moles present on the human body as potential
melanoma risk factors. In this document, we consider the challenging task
of applying a multi-instance learning (MIL) algorithm for discriminating
melanoma from dysplastic nevi and outline an even more complex chal-
lenge related to the classification of dysplastic nevi from common nevi.
Since the results appear promising, we conclude that a MIL technique
could be at the basis of tools useful for skin lesion detection.
Keywords: Image Classification · Multiple Instance Learning · Dysplas-
tic nevi Detection.
1 Introduction
According to the latest report of the World Health Organization (WHO), in
2018 melanoma has caused over 60.000 deaths and over 280.000 new cases have
been diagnosed [1]. Despite the ever increasing diffusion and its aggressiveness, if
melanoma is identified by an early diagnosis it is a type of curable cancer. Some
clinical protocols such as the ABCDE rule [2] have been established to facilitate
the task of specialists in identifying the lesion from its initial phase. These clinical
Copyright c 2020 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0). This volume is published
and copyrighted by its editors. SEBD 2020, June 21-24, 2020, Villasimius, Italy.
protocols take into consideration features lesion such as asymmetry, irregular
edges, colors, diameters greater than 6 mm and evolving stages.
The importance of an early detection of melanoma have led research com-
munities to develop automatic frameworks called Computer Aided Diagnosis
(CAD) systems, for the analysis of skin lesions. CAD include steps such as im-
age acquisition, pre-processing, segmentation, features extraction and selection
and finally classification of lesions.
This work focuses on the classification task of discriminating melanoma from
dysplastic nevi. Some studies have shown that specific ethnic groups present a
great number of common and dysplastic nevi on their’s bodies surface [3].
Individuals with dysplastic nevi syndrome or dysplastic nevi with family his-
tory of melanoma face a greater risk of developing melanoma [4]. These premises
justify the perception that automatic diagnosis of skin lesions must consider, be-
sides the distinction between melanoma and common nevi, also the one between
melanoma and dysplastic nevi, that is more difficult due to the similarities of
the two type of lesions [5].
Nowadays, few studies are available on this specific topic, therefore this paper
is a contribution to this challenging task. Our basic idea takes its cue from the
work [6] in which the authors have shown how the use of simple color features,
on dermatoscopic images, lets to obtain satisfactory classification performances.
In the present paper, we apply a recent MIL approach [7, 8] on dermatoscopic
images using only color features to verify its effectiveness in the classification
steps concerning Melanomas vs Dyspastic Nevi and Dysplastic Nevi vs Common
ones. The paper is organized as follows. In the next section we focus on the role
that the presence of dysplastic nevi and common nevi may imply in terms of risk
of melanoma onset. In Section 3 we recall the peculiarities of a MIL approach,
focusing on the Lagrangian relaxation type algorithm proposed in [9]. In Section
4 some numerical results on dermoscopic images are presented and finally brief
conclusions are given in Section 5.
2 Dysplastic nevi
The term “dysplastic nevus” (DN) indicates a nevus with different histological
and genetic characteristics compared to common nevus. More specifically, the
term derives from the Greek “dis-” (bad or malfunction) and “-plasia” (develop-
ment of growth or change) [10] and indicates a potentially dangerous lesion for
his guest. Several studies have attempted to correlate the degree of dysplasia of
melanocytes with the risk of melanoma [11, 12].
Syndrome of dysplastic nevus (DNS) refers to subjects who have a high num-
ber of benign moles and dysplastic nevi. Dysplastic nevi are more likely to un-
dergo malignant transformation when they occur among members of melanoma
families.
In [13], the authors indicate a cumulative lifetime risk of almost 100% in
individuals who have dysplastic nevi and are related to melanoma; about 30%
of melanomas occur within atypical moles. In 40-50% of cases, there is a genetic
Fig. 1. Dermoscopic image of common nevus (a), dysplastic nevus (b) and melanoma
(c)
predisposition for the formation of melanoma. The onset of this skin cancer has
been associated with germline mutations in the CDKN2A gene, which encodes
p16 (a regulator of cell division).
In [14], some studies based on histological analysis have correlated the pres-
ence of dysplastic nevi with melanoma. The caution that should be observed in
correspondence with a diagnosis of a severe DNS should not be overlooked, as
it could represent a miss-diagnosed in situ melanoma [15].
Basically, there are two objective criteria that have been shown to be related
to the risk of melanoma:
– In [16], people with a number of nevi greater than 100 had a 7 times greater
risk of melanoma than those with a count of less than 15.
– The presence of large nevi increases the relative risk of melanoma. If these
nevi have a diameter less than 2.4 mm they have a relative risk of 1, while
the relative risk progressively increases up to 5 if the lesion has a diameter
greater than 4.4 mm [17].
Simultaneously with the definition of the exact cause-effect correlations, var-
ious solutions have been proposed over time for the automatic identification of
skin lesions.
In [18] the authors, depicts a summary of many recent proposed methods by
reporting the results in terms of sensitivity, specificity and dataset size. In the
same work methods are categorized based on their classification scope: melanoma
from benign (M vs B), melanoma from benign and dysplastic (M vs (B + D))
and melanoma versus only dysplastic nevi (M vs D).
The comparison among different approaches is far from being easy as each
proposal has been applied on different datasets and adopts different features
sets. As for the feature, an additional difference arise between global and lo-
cal features. Global features are extracted taking the lesion as a whole, while
local features are extracted from portions of the image. On a qualitative level,
AdaBoost (AdB), artificial neural network (ANN), Support Vector Machines
(SVM) appear to be the most effective methods [19]. To the best of our knowl-
edge, nobody takes into consideration the classification task of dysplastic nevi
against common nevi [20].
3 Multiple Instance learning
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. Even, the availability of partially
labeled data can be appropriately exploited using machine learning approaches
[21], [22].
In particular, Multiple Instance Learning scenario is 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.
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 difference
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.
A natural application of the Multiple Instance Learning is in diagnostics by
means of medical images, where we want to discriminate between non-healthy
and healthy patients on the basis of their medical scan (bag). According to
standard Mil assumption:
– if in the medical scan at least one sub-region (instance) is abnormal, then a
patient is positive;
– if in the medical scan all the subregions (instances) are normal, then the
patient is negative.
The classification of complex objects cannot be represented by single features
vector, i.e. by single instance: this is another reason why we propose to use MIL
techniques for melanoma detection.
With a MIL approach it is therefore possible to obtain global informa-
tion from local one. For further details and general considerations on the MIL
paradigm, we refer the reader to surveys [23]. In [24], a detailed review is given
concerning Multiple Instance Learning applied for medical images and video
analysis. The MIL approach, as far as we know, is still very rarely used for
melanoma detection, and has never been used for the detection of dysplastic
nevi.
4 Numerical experimentation
The MIL algorithm used for the classification task in this paper has been pro-
posed in [9], and has been tested for the classification of both dermoscopic
images [25, 26] taken from the PH2 database [27], and photographs datasets
publicly available from two online databases https://www.dermquest.com and
http://www.dermins.net [28]. The entire PH2 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 40 images of melanomas
(1.a), the 80 of dysplastic nevi (1.b) and the 80 of common nevi (1.c), without
taking into account the indications resulting from the manual analysis carried
out by the specialists.
The only criterion is adopted at image level by considering: (i) positive the
images related to melanomas and negative the ones related to dysplastic nevi
and (ii) positive the images related to dysplastic nevi and negative the ones
related to common nevi. Although pre-processing steps of the images allow for
better performance [29, 30], the images we used were not pre-processed, i.e. they
were not cleaned of the presence of noise, such as possible hair or halo left by
the dermoscopic gel used to allow better illumination of the lesions.
The MIL algorithm, referred to as MIL-RL, has been re-implemented in Mat-
lab and has been run on a Windows 10 system featuring a 2.21 GHz processor.
We have duplicated all the images of melanomas, adding a Gaussian noise with
zero mean with variance equal to 0.0001, as in the [6]. In this way we obtained
a balanced dataset containing three classes of data, Melanomas (M), Dysplastic
Nevi (DN) and Common Nevi (N) each with 80 images. The imbalance between
the classes of training datasets should not be underestimated. The risk consists
in undermining the classification performance of the models, which can manifest
over-fitting, thus losing in generalization. For our experiments we considered the
following two data set configurations:
– 160 images: 80 Melanomas vs 80 Dysplastic Nevi;
– 160 images: 80 Dysplastic Nevi vs 80 Common Nevi.
For each data set configuration, we performed two types of experiments,
using a five fold and a ten fold cross-validation. The respective results are listed
in Table 1 and Table 2, where we report the average of correctness, sensitivity,
specificity, F-score and CPU time.
The proposed MIL optimization model is of SVM type; in order to appreciate
the MIL classification paradigm, we report in the columns SVM and SVM-RBF
the results obtained using a standard SVM approach [31] with linear and RBF
kernels, respectively. For each data set configuration and for each experiment (5-
CV and 10-CV), the best results in Table 1 and Table 2 have been underlined.
From numerical experiments it emerges that, in general, MIL-RL overcomes
the SVM technique (with both linear and RBF kernels) in terms of correctness
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.
We observe that the CPU times of MIL-RL algorithm are better than those
recorded by linear SVM. Concerning the classification of dysplastic nevi against
common nevi, the performances of all three methods appear unsatisfactory. The
MIL-RL algorithm records the best values of accuracy and specificity, but overall
5-CV 10-CV
MIL-RL SVM SVM-RBF MIL-RL SVM SVM-RBF
Correctness (%) 87.50 72.50 85.63 86.25 69.38 86.25
Sensitivity (%) 92.56 77.21 87.06 91,08 69.65 87.88
Specificity (%) 81.50 67.51 85.51 82.12 69.87 85.95
F-score (%) 88.31 74.96 85.84 87.01 68.68 87.52
CPU time (secs) 0.90 1.84 0.04 1.20 2.05 0.03
Table 1. Average testing values: 80 melanomas and 80 dysplastic nevi
5-CV 10-CV
MIL-RL SVM SVM-RBF MIL-RL SVM SVM-RBF
Correctness (%) 60.63 60.00 49.38 59.38 58.13 51.88
Sensitivity (%) 35.07 54.91 53.58 31.77 43.67 58.92
Specificity (%) 84.86 67.58 46.97 87.06 73.48 46.47
F-score (%) 44.76 56.79 50.09 42.77 48.57 53.74
CPU time (secs) 1.38 1.95 0.01 1.71 2.13 0.03
Table 2. Average testing values: 80 dysplatic nevi and 80 common nevi
it is not effective to solve the proposed task. Better results could be obtained
using images pre-processing steps and by using further useful features [33, 34].
5 Conclusions
In this paper we present an application of a MIL approach for the detection
of melanoma by dysplastic nevi and of dysplastic nevi by common ones. These
two issues are not widespread in the literature. Anyhow, pathologies such as
the DNS require tools to support phisicians in diagnostic process and mobile
applications useful for promoting self diagnosis. To this end, we point out that it
is under implementation a module of the software Simpatico 3d that is in charge
of allowing self diagnosis [35, 36, 38, 40, 41]. The obtained results show that in
the first case the MIL approach is very promising, even using only color features
and with no pre-processing step.
In the second case, the MIL approach 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. One way we intend to pursue is to apply MIL ap-
proaches that use spherical separation surfaces. In particular, the algorithm [32]
seems to be an interesting proposal for the development of applications in which
positive and negative elements have strong similar characteristics.
Future research could include the design of more sophisticated segmentation
techniques in order to further improve classification results, as well as the appli-
cation of the proposed method in other medical fields [39, 37] to identify other
types of injuries.
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