1,929 research outputs found
BCN20000: dermoscopic lesions in the wild
This article summarizes the BCN20000 dataset, composed of 19424 dermoscopic images of skin lesions captured from 2010 to 2016 in the facilities of the Hospital ClĂnic in Barcelona. With this dataset, we aim to study the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions found in hard-to-diagnose locations (nails and mucosa), large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. The BCN20000 will be provided to the participants of the ISIC Challenge 2019 [8], where they will be asked to train algorithms to classify dermoscopic images of skin cancer automatically.Peer ReviewedPreprin
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
Systematic literature review of dermoscopic pigmented skin lesions classification using convolutional neural network (CNN)
The occurrence of pigmented skin lesions (PSL), including melanoma, are rising, and early detection is crucial for reducing mortality. To assist Pigmented skin lesions, including melanoma, are rising, and early detection is crucial in reducing mortality. To aid dermatologists in early detection, computational techniques have been developed. This research conducted a systematic literature review (SLR) to identify research goals, datasets, methodologies, and performance evaluation methods used in categorizing dermoscopic lesions. This review focuses on using convolutional neural networks (CNNs) in analyzing PSL. Based on specific inclusion and exclusion criteria, the review included 54 primary studies published on Scopus and PubMed between 2018 and 2022. The results showed that ResNet and self-developed CNN were used in 22% of the studies, followed by Ensemble at 20% and DenseNet at 9%. Public datasets such as ISIC 2019 were predominantly used, and 85% of the classifiers used were softmax. The findings suggest that the input, architecture, and output/feature modifications can enhance the model's performance, although improving sensitivity in multiclass classification remains a challenge. While there is no specific model approach to solve the problem in this area, we recommend simultaneously modifying the three clusters to improve the model's performance
Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review
Skin lesions known as naevi exhibit diverse characteristics such as size,
shape, and colouration. The concept of an "Ugly Duckling Naevus" comes into
play when monitoring for melanoma, referring to a lesion with distinctive
features that sets it apart from other lesions in the vicinity. As lesions
within the same individual typically share similarities and follow a
predictable pattern, an ugly duckling naevus stands out as unusual and may
indicate the presence of a cancerous melanoma. Computer-aided diagnosis (CAD)
has become a significant player in the research and development field, as it
combines machine learning techniques with a variety of patient analysis
methods. Its aim is to increase accuracy and simplify decision-making, all
while responding to the shortage of specialized professionals. These automated
systems are especially important in skin cancer diagnosis where specialist
availability is limited. As a result, their use could lead to life-saving
benefits and cost reductions within healthcare. Given the drastic change in
survival when comparing early stage to late-stage melanoma, early detection is
vital for effective treatment and patient outcomes. Machine learning (ML) and
deep learning (DL) techniques have gained popularity in skin cancer
classification, effectively addressing challenges, and providing results
equivalent to that of specialists. This article extensively covers modern
Machine Learning and Deep Learning algorithms for detecting melanoma and
suspicious naevi. It begins with general information on skin cancer and
different types of naevi, then introduces AI, ML, DL, and CAD. The article then
discusses the successful applications of various ML techniques like
convolutional neural networks (CNN) for melanoma detection compared to
dermatologists' performance. Lastly, it examines ML methods for UD naevus
detection and identifying suspicious naevi
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