[論文レビュー] An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning

Nabin K. Mishra, M. Emre Celebi|arXiv (Cornell University)|Jan 28, 2016

Cutaneous Melanoma Detection and Management被引用数 107

ひとこと要約

皮膚検査画像における自動 melanoma 検出の総説で、病変のセグメンテーション、特徴抽出、機械学習による分類を概説し、臨床導入に向けた課題を指摘する。

ABSTRACT

The incidence of malignant melanoma continues to increase worldwide. This cancer can strike at any age; it is one of the leading causes of loss of life in young persons. Since this cancer is visible on the skin, it is potentially detectable at a very early stage when it is curable. New developments have converged to make fully automatic early melanoma detection a real possibility. First, the advent of dermoscopy has enabled a dramatic boost in clinical diagnostic ability to the point that melanoma can be detected in the clinic at the very earliest stages. The global adoption of this technology has allowed accumulation of large collections of dermoscopy images of melanomas and benign lesions validated by histopathology. The development of advanced technologies in the areas of image processing and machine learning have given us the ability to allow distinction of malignant melanoma from the many benign mimics that require no biopsy. These new technologies should allow not only earlier detection of melanoma, but also reduction of the large number of needless and costly biopsy procedures. Although some of the new systems reported for these technologies have shown promise in preliminary trials, widespread implementation must await further technical progress in accuracy and reproducibility. In this paper, we provide an overview of computerized detection of melanoma in dermoscopy images. First, we discuss the various aspects of lesion segmentation. Then, we provide a brief overview of clinical feature segmentation. Finally, we discuss the classification stage where machine learning algorithms are applied to the attributes generated from the segmented features to predict the existence of melanoma.

研究の動機と目的

• 臨床診断への影響について議論しつつ、早期の melanoma 検出のための dermoscopy の利用を動機づける。
• dermoscopy 画像を分析するための画像処理手順を要約し、特に病変のセグメンテーションに焦点を当てる。
• 臨床的特徴がどのようにセグメンテーションされ、機械学習可能な属性へ変換されるかをレビューする。
• セグメント化された特徴に適用された機械学習分類器を用いた melanoma 予測について論じる。
• 現在の制限点と精度・再現性・より広範な適用の必要性を強調する。

提案手法

• Describe the role of dermoscopy in enabling early melanoma detection.
• Outline lesion segmentation approaches in dermoscopy images.
• Provide an overview of clinical feature segmentation for dermoscopy.
• Discuss the pipeline from segmented features to machine learning-based classification.
• Review machine learning algorithms used to predict melanoma from extracted features.
• Address challenges related to accuracy, reproducibility, and clinical adoption.

実験結果

リサーチクエスチョン

• RQ1 dermoscopy 病変に対する主要なセグメンテーション手法とその有効性は何か？
• RQ2 dermoscopy 画像から抽出される臨床的特徴は何で、それらが機械学習の属性へどのように変換されるのか？
• RQ3 dermoscopy由来の特徴から melanoma を予測するのに適した機械学習分類器はどれか？
• RQ4 精度と再現性に関して広範な臨床普及の障壁は何か？
• RQ5 不要な生検を減らすために、dermoscopy 画像における自動 melanoma 検出をさらにどのように改善できるか？

主な発見

• Dermoscopy は早期 melanoma 検出と診断能力の向上を促進してきた。
• Advances in image processing enable automatic distinction between melanoma and benign mimics.
• Current systems show promise in preliminary trials but require improvements in accuracy and reproducibility for widespread use.
• Development of fully automatic detection is feasible but not yet ready for universal clinical implementation.
• The pipeline from segmentation to classification is central to melanoma prediction in dermoscopy images.
• There is a need to reduce unnecessary and costly biopsies through reliable automation.