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Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

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Deep Learning Approach to Classify Cutaneous Melanoma in a Whole Slide Image.

Meng Li¹, Makoto Abe², Shigeo Nakano³

¹Medmain Research, Medmain Inc., Fukuoka 810-0042, Japan.

|March 29, 2023

Summary

Deep learning shows promise in automating the diagnosis of cutaneous melanoma from whole-slide images (WSIs). This AI tool can assist pathologists, improving diagnostic accuracy for challenging melanocytic lesions.

Keywords:

cancer screening deep learning melanoma whole slide image

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Dermatopathology
Computational pathology
Artificial intelligence in medicine

Background:

Histopathological diagnosis of cutaneous melanocytic lesions, while generally accurate, faces challenges with certain cases like melanoma.
Diagnostic controversies arise with melanoma mimicking benign nevi, amelanotic features, or in situ presentations.
Clinical-pathological correlation is the established gold standard for melanoma diagnosis.

Purpose of the Study:

To investigate the feasibility of applying deep learning algorithms for the automated classification of cutaneous melanoma.
To develop and evaluate AI models capable of assisting surgical pathologists in diagnosing melanocytic lesions from whole-slide images (WSIs).

Main Methods:

A dataset of 66 whole-slide images (WSIs), comprising 33 melanomas and 33 non-melanomas, was utilized for model training.
Weakly supervised learning techniques were employed to train the deep learning models.
Model performance was assessed on an independent test set of 90 WSIs (40 melanomas, 50 non-melanomas).

Main Results:

The best performing deep learning model achieved an Area Under the Receiver Operating Characteristic Curve (ROC-AUC) of 0.821 at the whole-slide image (WSI) level.
At the tile level, the model demonstrated a higher ROC-AUC of 0.936, indicating strong performance in identifying melanoma at a finer resolution.
These preliminary results suggest significant potential for AI in melanoma classification.

Conclusions:

Deep learning models can effectively classify cutaneous melanoma in whole-slide images (WSIs).
The developed AI approach shows potential as a valuable tool to support surgical pathologists, particularly in complex or time-consuming cases.
Further research and validation are warranted to integrate this technology into routine diagnostic workflows.