46 research outputs found
Skin lesion image segmentation using Delaunay Triangulation for melanoma detection
Developing automatic diagnostic tools for the early detection of skin cancer
lesions in dermoscopic images can help to reduce melanoma-induced mortal-
ity. Image segmentation is a key step in the automated skin lesion diagnosis
pipeline. In this paper, a fast and fully-automatic algorithm for skin lesion
segmentation in dermoscopic images is presented. Delaunay Triangulation is
used to extract a binary mask of the lesion region, without the need of any
training stage. A quantitative experimental evaluation has been conducted
on a publicly available database, by taking into account six well-known state-
of-the-art segmentation methods for comparison. The results of the experi-
mental analysis demonstrate that the proposed approach is highly accurate
when dealing with benign lesions, while the segmentation accuracy signi-
cantly decreases when melanoma images are processed. This behavior led us
to consider geometrical and color features extracted from the binary masks
generated by our algorithm for classication, achieving promising results for
melanoma detection
Computer aided diagnosis system using dermatoscopical image
Computer Aided Diagnosis (CAD) systems for melanoma detection aim to mirror the expert
dermatologist decision when watching a dermoscopic or clinical image. Computer Vision
techniques, which can be based on expert knowledge or not, are used to characterize the
lesion image. This information is delivered to a machine learning algorithm, which gives a
diagnosis suggestion as an output.
This research is included into this field, and addresses the objective of implementing a
complete CAD system using ‘state of the art’ descriptors and dermoscopy images as input.
Some of them are based on expert knowledge and others are typical in a wide variety of
problems. Images are initially transformed into oRGB, a perceptual color space, looking for
both enhancing the information that images provide and giving human perception to machine
algorithms. Feature selection is also performed to find features that really contribute to
discriminate between benign and malignant pigmented skin lesions (PSL). The problem of
robust model fitting versus statistically significant system evaluation is critical when working
with small datasets, which is indeed the case. This topic is not generally considered in works
related to PSLs. Consequently, a method that optimizes the compromise between these two
goals is proposed, giving non-overfitted models and statistically significant measures of
performance. In this manner, different systems can be compared in a fairer way. A database
which enjoys wide international acceptance among dermatologists is used for the
experiments.IngenierÃa de Sistemas Audiovisuale
On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors
Deep learning based medical image classifiers have shown remarkable prowess
in various application areas like ophthalmology, dermatology, pathology, and
radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD)
systems in real clinical setups is severely limited primarily because their
decision-making process remains largely obscure. This work aims at elucidating
a deep learning based medical image classifier by verifying that the model
learns and utilizes similar disease-related concepts as described and employed
by dermatologists. We used a well-trained and high performing neural network
developed by REasoning for COmplex Data (RECOD) Lab for classification of three
skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and
performed a detailed analysis on its latent space. Two well established and
publicly available skin disease datasets, PH2 and derm7pt, are used for
experimentation. Human understandable concepts are mapped to RECOD image
classification model with the help of Concept Activation Vectors (CAVs),
introducing a novel training and significance testing paradigm for CAVs. Our
results on an independent evaluation set clearly shows that the classifier
learns and encodes human understandable concepts in its latent representation.
Additionally, TCAV scores (Testing with CAVs) suggest that the neural network
indeed makes use of disease-related concepts in the correct way when making
predictions. We anticipate that this work can not only increase confidence of
medical practitioners on CAD but also serve as a stepping stone for further
development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural
Networks (IJCNN) 202
Coherent Concept-based Explanations in Medical Image and Its Application to Skin Lesion Diagnosis
Early detection of melanoma is crucial for preventing severe complications
and increasing the chances of successful treatment. Existing deep learning
approaches for melanoma skin lesion diagnosis are deemed black-box models, as
they omit the rationale behind the model prediction, compromising the
trustworthiness and acceptability of these diagnostic methods. Attempts to
provide concept-based explanations are based on post-hoc approaches, which
depend on an additional model to derive interpretations. In this paper, we
propose an inherently interpretable framework to improve the interpretability
of concept-based models by incorporating a hard attention mechanism and a
coherence loss term to assure the visual coherence of concept activations by
the concept encoder, without requiring the supervision of additional
annotations. The proposed framework explains its decision in terms of
human-interpretable concepts and their respective contribution to the final
prediction, as well as a visual interpretation of the locations where the
concept is present in the image. Experiments on skin image datasets demonstrate
that our method outperforms existing black-box and concept-based models for
skin lesion classification.Comment: Under IEEE Copyright. Accepted for publication at CVPR 2023 Workshop
Safe Artificial Intelligence for Automated Driving (SAIAD
Computer-Aided Diagnosis for Melanoma using Ontology and Deep Learning Approaches
The emergence of deep-learning algorithms provides great potential to enhance the prediction performance of computer-aided supporting diagnosis systems. Recent research efforts indicated that well-trained algorithms could achieve the accuracy level of experienced senior clinicians in the Dermatology field. However, the lack of interpretability and transparency hinders the algorithms’ utility in real-life. Physicians and patients require a certain level of interpretability for them to accept and trust the results. Another limitation of AI algorithms is the lack of consideration of other information related to the disease diagnosis, for example some typical dermoscopic features and diagnostic guidelines. Clinical guidelines for skin disease diagnosis are designed based on dermoscopic features. However, a structured and standard representation of the relevant knowledge in the skin disease domain is lacking.
To address the above challenges, this dissertation builds an ontology capable of formally representing the knowledge of dermoscopic features and develops an explainable deep learning model able to diagnose skin diseases and dermoscopic features. Additionally, large-scale, unlabeled datasets can learn from the trained model and automate the feature generation process. The computer vision aided feature extraction algorithms are combined with the deep learning model to improve the overall classification accuracy and save manual annotation efforts
Automatic Detection of Blue-White Veil and Related Structures in Dermoscopy Images
Dermoscopy is a non-invasive skin imaging technique, which permits
visualization of features of pigmented melanocytic neoplasms that are not
discernable by examination with the naked eye. One of the most important
features for the diagnosis of melanoma in dermoscopy images is the blue-white
veil (irregular, structureless areas of confluent blue pigmentation with an
overlying white "ground-glass" film). In this article, we present a machine
learning approach to the detection of blue-white veil and related structures in
dermoscopy images. The method involves contextual pixel classification using a
decision tree classifier. The percentage of blue-white areas detected in a
lesion combined with a simple shape descriptor yielded a sensitivity of 69.35%
and a specificity of 89.97% on a set of 545 dermoscopy images. The sensitivity
rises to 78.20% for detection of blue veil in those cases where it is a primary
feature for melanoma recognition
Review on automatic early skin cancer detection
Skin cancer is increasing in different countries especially in Australia. Early detection of skin cancer can treat melanoma successfully, therefore, curability and survival depends directly on removing melanoma in its early stages. Since clinical observations face to different fault for melanoma detection, the automatic diagnosis can help to increase the accuracy of detection. Reviewing the researches have done in skin cancer detection and providing the overview on automatic detection of skin cancer are the ultimate aims of this paper. It presents the literature on automatic skin cancer detection and describes the different steps of such process. © 2011 IEEE