442 research outputs found
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
Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet
Skin cancer, a major form of cancer, is a critical public health problem with
123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma
cases worldwide each year. The leading cause of skin cancer is high exposure of
skin cells to UV radiation, which can damage the DNA inside skin cells leading
to uncontrolled growth of skin cells. Skin cancer is primarily diagnosed
visually employing clinical screening, a biopsy, dermoscopic analysis, and
histopathological examination. It has been demonstrated that the dermoscopic
analysis in the hands of inexperienced dermatologists may cause a reduction in
diagnostic accuracy. Early detection and screening of skin cancer have the
potential to reduce mortality and morbidity. Previous studies have shown Deep
Learning ability to perform better than human experts in several visual
recognition tasks. In this paper, we propose an efficient seven-way automated
multi-class skin cancer classification system having performance comparable
with expert dermatologists. We used a pretrained MobileNet model to train over
HAM10000 dataset using transfer learning. The model classifies skin lesion
image with a categorical accuracy of 83.1 percent, top2 accuracy of 91.36
percent and top3 accuracy of 95.34 percent. The weighted average of precision,
recall, and f1-score were found to be 0.89, 0.83, and 0.83 respectively. The
model has been deployed as a web application for public use at
(https://saketchaturvedi.github.io). This fast, expansible method holds the
potential for substantial clinical impact, including broadening the scope of
primary care practice and augmenting clinical decision-making for dermatology
specialists.Comment: This is a pre-copyedited version of a contribution published in
Advances in Intelligent Systems and Computing, Hassanien A., Bhatnagar R.,
Darwish A. (eds) published by Chaturvedi S.S., Gupta K., Prasad P.S. The
definitive authentication version is available online via
https://doi.org/10.1007/978-981-15-3383-9_1
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
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
Computer aided diagnostic support system for skin cancer: A review of techniques and algorithms
Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided. © 2013 Ammara Masood and Adel Ali Al-Jumaily
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