Knowledge Transfer for Melanoma Screening with Deep Learning (1703.07479v1)

Abstract: Knowledge transfer impacts the performance of deep learning -- the state of the art for image classification tasks, including automated melanoma screening. Deep learning's greed for large amounts of training data poses a challenge for medical tasks, which we can alleviate by recycling knowledge from models trained on different tasks, in a scheme called transfer learning. Although much of the best art on automated melanoma screening employs some form of transfer learning, a systematic evaluation was missing. Here we investigate the presence of transfer, from which task the transfer is sourced, and the application of fine tuning (i.e., retraining of the deep learning model after transfer). We also test the impact of picking deeper (and more expensive) models. Our results favor deeper models, pre-trained over ImageNet, with fine-tuning, reaching an AUC of 80.7% and 84.5% for the two skin-lesion datasets evaluated.

• The paper establishes that transfer learning from ImageNet significantly improves melanoma screening, yielding AUC scores up to 84.5%.
• It demonstrates that fine-tuning pre-trained models on specific melanoma datasets further enhances diagnostic performance.
• The study highlights the advantage of using large, heterogeneous datasets over smaller, medical-specific ones to optimize deep learning models.

Knowledge Transfer for Melanoma Screening with Deep Learning

The application of deep learning (DL) in medical imaging has demonstrated considerable potential, particularly in the domain of melanoma screening, where the need for accurate, early diagnosis is critical. The paper "Knowledge Transfer for Melanoma Screening with Deep Learning" explores the efficacy of transfer learning in enhancing DL models applied to melanoma screening. The paper centers on the transfer of knowledge from more general datasets, like ImageNet, and from related domains, such as diabetic retinopathy, to improve model performance on specific melanoma datasets.

Investigative Focus

The paper seeks to fill a gap in systematic evaluations of transfer learning within the context of automated melanoma screening. The authors address three primary questions:

1. Impact of Transfer Learning: Despite the consensus on its benefits, quantifying the improvements brought by transfer learning necessitates empirical evidence.
2. Optimal Source for Transfer Learning: The authors compare the efficacy of transfer learning from general, large-scale datasets like ImageNet against smaller, yet medically-oriented datasets like diabetic retinopathy images.
3. Effects of Fine Tuning: They evaluate the impact of fine-tuning transferred models on the target task to assess whether additional retraining enhances performance.

Methodology

The research employs datasets from the Interactive Atlas of Dermoscopy and the ISIC Skin Lesion Analysis datasets as target datasets. Models are pre-trained on the ImageNet and Kaggle Diabetic Retinopathy datasets, representing generalized and domain-specific data sources, respectively. The paper rigorously investigates different experimental designs, including multi-class and binary classifications. Performance metrics such as AUC are used to benchmark performance across various configurations and conditions.

Results and Insights

• Model Performance: Pre-trained models on ImageNet, followed by fine-tuning, consistently outperformed other configurations, achieving AUC scores of 80.7% and 84.5% across tested datasets.
• Role of Transfer Learning: The analysis confirms the superiority of transfer learning from large, heterogeneous datasets over smaller, domain-specific ones, challenging the expectation of enhanced performance from the latter.
• Impact of Fine-tuning: The inclusion of fine-tuning emerged as a critical step, enhancing model adaptability to specific data characteristics pertinent to melanoma screening.

Tables presented in the paper illustrate that deeper networks like VGG-16 yielded better results compared to shallower architectures such as VGG-M, highlighting the importance of network depth in transfer learning tasks. Furthermore, segregating cancerous types affected results, suggesting that employing structured class definitions might aid model precision.

Implications and Future Research

The research underscores the practical and theoretical importance of understanding transfer learning dynamics in medical applications. The findings advocate for utilizing general large-scale datasets as the base for transfer learning, potentially influencing model training protocols and dataset selections in future AI research in medical imaging.

Future work could explore additional medical datasets to enhance specificity or alternative transfer schemes to optimize the trade-offs between model specialization and generalization. Moreover, the paper indicates that more nuanced designs in classifying different melanoma types could refine diagnostic support systems by maximizing predictability for case stratification, particularly for difficult diagnoses.

In conclusion, the paper provides empirical clarity on the application of transfer learning in melanoma screening, offering insights into optimizing DL models for nuanced medical tasks. The findings hold significant implications for AI advancements in healthcare, pressing for continued exploration into model transfer techniques and architectures to upscale diagnostic capabilities within limited data environments.