Feature-conditioned neural network pre-training for skin cancer classification.

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Abstract/Contents

Abstract: Despite tremendous advances in image recognition using convolutional neural networks over the past few years, the performance of deep learning based models on supervised classification is typically benchmarked using enormous labeled datasets comprising millions of images. Realistically, for many highly impactful classification tasks, such as medical image analysis, obtaining sufficient labeled data may be prohibitively expensive, time-consuming, or even impossible. However, it is often easier to obtain unlabeled image datasets, along with con-textual information that is related to the true desired labels. In this work, we propose two separate pre-training algorithms to leverage these datasets a GAN-based pre-training algorithm, and a purely CNN-based algorithm and evaluate their performance specifically for binary classification of skin lesions as either benign moles or cancerous melanomas. When tested against traditional model pre-training using the ImageNet dataset, both pre-training algorithms boost performance on the classification task, especially when smaller labeled datasets of only 5,000-10,000 images are available for training. Additionally, using the feature conditioned CNN-based pre-training algorithm, subsequent model performance exceeds both dermatologist and previous state-of-the-art results when evaluated on a test set of dermoscopic skin lesion images.

Type of resource	text
Date created	2017

Subject	Ben Wegbreit Prize for Best Undergraduate Honors Thesis
Subject	Artificial Intelligence
Subject	Computer Science
Genre	Thesis

Location	https://purl.stanford.edu/nz411hx4576

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License: This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

Preferred Citation: Wong, Catherine. (2017). Feature-conditioned neural network pre-training for skin cancer classification. Stanford Digital Repository. Available at: https://purl.stanford.edu/nz411hx4576

Undergraduate Theses, School of Engineering

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