Dermatopathology is a sub-specialty of dermatology that involves
microscopic examination of skin biopsy information for clinical diagnosis. While
knowledge of disease and skill in diagnosis have improved with advances in
technology and science, the amount of clinical information and the quality of biopsy
specimens remains a challenge. Typically, the specimens are biopsied, and the analyses
are pathologic, serving a supportive or confirming role in the diagnostic algorithm.
Automation and artificial intelligence in dermatopathology provide lower human
mistakes, higher efficiency and productivity, improved traceability, uptake of digital
pathology, and cost savings. Machine learning algorithms essentially fall into three
categories: supervised learning algorithms, unsupervised learning algorithms, and
semi-supervised learning algorithms. Regression algorithms discover the connection
between target output variables and input features to predict output for new instances.
In dermatology, AI can be used to detect skin cancer, especially melanoma. AI models
can then accurately learn all the patterns and features that are indicative of malignancy
from a large dataset of labelled skin lesion images. However, the implementation of
such systems utilising artificial intelligence is wholly dependent on the availability of
high-quality and diverse training data. By applying advanced neural architectures to
clinical, dermoscopic secretions, or histopathological features, AI models may stratify
patients with melanoma into high- and low-risk cohorts and tailor management and
surveillance accordingly. Image preprocessing is a crucial step in the dermatopathology
pipeline to guarantee high image quality and uniformity. One of the most important
strategies in dermatopathology is data augmentation, which expands the data without
further image acquisition. We can also include advanced augmentation techniques that
can vary skin lesions with rotation, flipping and scaling. Annotation and labelling are
needed for crafting a machine learning model, but they are impossible to avoid, as they provide such models with the essential nature of the image. Techniques, as you might
have seen, oversampling or undersampling the dataset, are pretty useful because we
want our model to have enough exposure to less frequent cases present in a balanced
dataset to ensure the models that we build are robust ones. Statistical approaches
involving cross-validation and hyperparameter tuning can be used to estimate the skill
of machine learning models. There are many evaluation metrics based on which your
machine learning models are trained, such as accuracy, precision, recall, F1 score,
ROC-AUC, etc., which are all short to better understand your ML models.
Keywords: Clinical practice integration, Convolutional neural networks (CNNs), Dermatopathology, Machine learning, Predictive analytics, Random forests, Support vector machines (SVMs).
