Fully Convolutional Network for Melanoma Diagnostics

2018/06/12 by Adon Phillips, Phillips, Adon, Iris Teo +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #AI in cancer detection #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Cutaneous Melanoma Detection and Management #FOS: Computer and information sciences #cs.CV

paper · pdf · doi:10.48550/arxiv.1806.04765

arxiv created 2018/06/12 · openalex publication_date 2018/06/12 · arxiv updated 2018/06/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This work seeks to determine how modern machine learning techniques may be applied to the previously unexplored topic of melanoma diagnostics using digital pathology. We curated a new dataset of 50 patient cases of cutaneous melanoma using digital pathology. We provide gold standard annotations for three tissue types (tumour, epidermis, and dermis) which are important for the prognostic measurements known as Breslow thickness and Clark level. Then, we devised a novel multi-stride fully convolutional network (FCN) architecture that outperformed other networks trained and evaluated using the same data according to standard metrics. Finally, we trained a model to detect and localize the target tissue types. When processing previously unseen cases, our model's output is qualitatively very similar to the gold standard. In addition to the standard metrics computed as a baseline for our approach, we asked three additional pathologists to measure the Breslow thickness on the network's output. Their responses were diagnostically equivalent to the ground truth measurements, and when removing cases where a measurement was not appropriate, inter-rater reliability (IRR) between the four pathologists was 75.0%. Given the qualitative and quantitative results, it is possible to overcome the discriminative challenges of the skin and tumour anatomy for segmentation using modern machine learning techniques, though more work is required to improve the network's performance on dermis segmentation. Further, we show that it is possible to achieve a level of accuracy required to manually perform the Breslow thickness measurement.

Citations

• Robbins and Cotran Pathologic Basis of Disease
• The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks
• Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation
• Caffe: Convolutional Architecture for Fast Feature Embedding
• Feedforward semantic segmentation with zoom-out features

Related

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• Skin Lesion Analysis Towards Melanoma Detection via End-to-end Deep Learning of Convolutional Neural Networks
• AI outperformed every dermatologist: Improved dermoscopic melanoma diagnosis through customizing batch logic and loss function in an optimized Deep CNN architecture
• Robust Melanoma Thickness Prediction via Deep Transfer Learning enhanced by XAI Techniques