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

Lacunarity Analysis: A Promising Method for the Automated Assessment of Melanocytic Naevi and Melanoma

The early diagnosis of melanoma is critical to achieving reduced mortality and increased survival. Although clinical examination is currently the method of choice for melanocytic lesion assessment, there is a growing interest among clinicians regarding the potential diagnostic utility of computerised image analysis. Recognising that there exist significant shortcomings in currently available algorithms, we are motivated to investigate the utility of lacunarity, a simple statistical measure previously used in geology and other fields for the analysis of fractal and multi-scaled images, in the automated assessment of melanocytic naevi and melanoma. Digitised dermoscopic images of 111 benign melanocytic naevi, 99 dysplastic naevi and 102 melanomas were obtained over the period 2003 to 2008, and subject to lacunarity analysis. We found the lacunarity algorithm could accurately distinguish melanoma from benign melanocytic naevi or non-melanoma without introducing many of the limitations associated with other previously reported diagnostic algorithms. Lacunarity analysis suggests an ordering of irregularity in melanocytic lesions, and we suggest the clinical application of this ordering may have utility in the naked-eye dermoscopic diagnosis of early melanoma

Hyperspectral Imaging Reveals Spectral Differences and Can Distinguish Malignant Melanoma from Pigmented Basal Cell Carcinomas : A Pilot Study

Pigmented basal cell carcinomas can be difficult to distinguish from melanocytic tumours. Hyperspectral imaging is a non-invasive imaging technique that measures the reflectance spectra of skin in vivo. The aim of this prospective pilot study was to use a convolutional neural network classifier in hyperspectral images for differential diagnosis between pigmented basal cell carcinomas and melanoma. A total of 26 pigmented lesions (10 pigmented basal cell carcinomas, 12 melanomas in situ, 4 invasive melanomas) were imaged with hyperspectral imaging and excised for histopatho-logical diagnosis. For 2-class classifier (melano-cytic tumours vs pigmented basal cell carcinomas) using the majority of the pixels to predict the class of the whole lesion, the results showed a sensitivity of 100% (95% confidence interval 81-100%), specificity of 90% (95% confidence interval 60-98%) and positive predictive value of 94% (95% confidence interval 73-99%). These results indicate that a convolutional neural network classifier can differentiate melanocytic tumours from pigmented basal cell carcinomas in hyperspectral images. Further studies are warranted in order to confirm these preliminary results, using larger samples and multiple tumour types, including all types of melanocytic lesions.Peer reviewe

Visual feature extraction from dermoscopic colour images for classification of melanocytic skin lesions

The early diagnosis of Melanoma is a challenging task for dermatologists, because of the characteristic similarities of Melanoma with other skin lesions such as typical moles and dysplastic nevi. Aims: This work aims to help both experienced and non-experienced dermatologists in the early detection of cutaneous Melanoma through the development of a computational helping tool based on the “ABCD” rule of dermoscopy. Moreover, it aims to decrease the need for invasive biopsy procedure for each tested abnormal skin lesion. Methods: This is accomplished through the utilization of MATLAB programming language to build a feature extraction tool for the assessment of lesion asymmetry, borders irregularity, and colors variation in the tested lesion. Results: The helping tool obtained a sensitivity of 81.48%, a specificity of 52.83% and accuracy of 62.50% in the assessment of the Asymmetry Index. A new metric for the borders irregularity index was built. Finally, for the Colors Variation Index algorithm a sensitivity of 51.37%, a specificity of 61.51% and accuracy of 57.80% was achieved. Conclusions: This work created a computational tool based on the ABCD-rule, which is helpful for both experienced and non-experienced dermatologists in the early discrimination of Melanoma than other types of skin lesions and to eliminate the need of the biopsy procedure. A new metric for the Borders Irregularity Index was established depending on the number of inflection points in the lesion’s borders

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

Technological Advances in the Diagnosis and Management of Pigmented Fundus Tumours

Choroidal naevi are the most common intraocular tumour. They can be pigmented or non-pigmented and have a predilection for the posterior uvea. The majority remain undetected and cause no harm but are increasingly found on routine community optometry examinations. Rarely does a naevus demonstrate growth or the onset of suspicious features to fulfil the criteria for a malignant melanoma. Because of this very small risk, optometrists commonly refer these patients to hospital eye units for a second opinion, triggering specialist examination and investigation, causing significant anxiety to patients and stretching medical resources. This PhD thesis introduces the MOLES acronym and scoring system that has been devised to categorise the risk of malignancy in choroidal melanocytic tumours according to Mushroom tumour shape, Orange pigment, Large tumour size, Enlarging tumour and Subretinal fluid. This is a simplified system that can be used without sophisticated imaging, and hence its main utility lies in the screening of patients with choroidal pigmented lesions in the community and general ophthalmology clinics. Under this system, lesions were categorised by a scoring system as ‘common naevus’, ‘low-risk naevus’, ‘high-risk naevus’ and ‘probable melanoma.’ According to the sum total of the scores, the MOLES system correlates well with ocular oncologists’ final diagnosis. The PhD thesis also describes a model of managing such lesions in a virtual pathway, showing that images of choroidal naevi evaluated remotely using a decision-making algorithm by masked non-medical graders or masked ophthalmologists is safe. This work prospectively validates a virtual naevus clinic model focusing on patient safety as the primary consideration. The idea of a virtual naevus clinic as a fast, one-stop, streamlined and comprehensive service is attractive for patients and healthcare systems, including an optimised patient experience with reduced delays and inconvenience from repeated visits. A safe, standardised model ensures homogeneous management of cases, appropriate and prompt return of care closer to home to community-based optometrists. This research work and strategies, such as the MOLES scoring system for triage, could empower community-based providers to deliver management of benign choroidal naevi without referral to specialist units. Based on the positive outcome of this prospective study and the MOLES studies, a ‘Virtual Naevus Clinic’ has been designed and adapted at Moorfields Eye Hospital (MEH) to prove its feasibility as a response to the COVID-19 pandemic, and with the purpose of reducing in-hospital patient journey times and increasing the capacity of the naevus clinics, while providing safe and efficient clinical care for patients. This PhD chapter describes the design, pathways, and operating procedures for the digitally enabled naevus clinics in Moorfields Eye Hospital, including what this service provides and how it will be delivered and supported. The author will share the current experience and future plan. Finally, the PhD thesis will cover a chapter that discusses the potential role of artificial intelligence (AI) in differentiating benign choroidal naevus from choroidal melanoma. The published clinical and imaging risk factors for malignant transformation of choroidal naevus will be reviewed in the context of how AI applied to existing ophthalmic imaging systems might be able to determine features on medical images in an automated way. The thesis will include current knowledge to date and describe potential benefits, limitations and key issues that could arise with this technology in the ophthalmic field. Regulatory concerns will be addressed with possible solutions on how AI could be implemented in clinical practice and embedded into existing imaging technology with the potential to improve patient care and the diagnostic process. The PhD will also explore the feasibility of developed automated deep learning models and investigate the performance of these models in diagnosing choroidal naevomelanocytic lesions based on medical imaging, including colour fundus and autofluorescence fundus photographs. This research aimed to determine the sensitivity and specificity of an automated deep learning algorithm used for binary classification to differentiate choroidal melanomas from choroidal naevi and prove that a differentiation concept utilising a machine learning algorithm is feasible

The role of AI classifiers in skin cancer images

Background: The use of different imaging modalities to assist in skin cancer diagnosis is a common practice in clinical scenarios. Different features representative of the lesion under evaluation can be retrieved from image analysis and processing. However, the integration and understanding of these additional parameters can be a challenging task for physicians, so artificial intelligence (AI) methods can be implemented to assist in this process. This bibliographic research was performed with the goal of assessing the current applications of AI algorithms as an assistive tool in skin cancer diagnosis, based on information retrieved from different imaging modalities. Materials and methods: The bibliography databases ISI Web of Science, PubMed and Scopus were used for the literature search, with the combination of keywords: skin cancer, skin neoplasm, imaging and classification methods. Results: The search resulted in 526 publications, which underwent a screening process, considering the established eligibility criteria. After screening, only 65 were qualified for revision. Conclusion: Different imaging modalities have already been coupled with AI methods, particularly dermoscopy for melanoma recognition. Learners based on support vector machines seem to be the preferred option. Future work should focus on image analysis, processing stages and image fusion assuring the best possible classification outcome.info:eu-repo/semantics/publishedVersio