# Detection and Localization of Melanoma Skin Cancer in Histopathological Whole Slide Images

arXiv: https://arxiv.org/abs/2302.03014 · HTML: https://mlsanity.com/paper/2302.03014/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 6 Feb 2023.

## Builds on (most cited first, 3 of 3)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendations](https://mlsanity.com/paper/2106.13689/) (2021)
- [Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection](https://mlsanity.com/paper/2203.10039/) (2022)

## Built on by (most cited first, 6 of 6)

- [Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs](https://mlsanity.com/paper/2403.07743/) (2024)
- [Vision Transformers for Small Histological Datasets Learned through Knowledge Distillation](https://mlsanity.com/paper/2305.17370/) (2023)
- [Precision in Building Extraction: Comparing Shallow and Deep Models using LiDAR Data](https://mlsanity.com/paper/2309.12027/) (2023)
- [Enhancing Prostate Cancer Diagnosis with Deep Learning: A Study using mpMRI Segmentation and Classification](https://mlsanity.com/paper/2310.05371/) (2023)
- [A Dual Convolutional Neural Network Pipeline for Melanoma Diagnostics and Prognostics](https://mlsanity.com/paper/2312.08766/) (2023)
- [Contrastive Deep Learning Reveals Age Biomarkers in Histopathological Skin Biopsies](https://mlsanity.com/paper/2411.16956/) (2024)

## Abstract (arXiv)

Melanoma diagnosed and treated in its early stages can increase the survival rate. A projected increase in skin cancer incidents and a dearth of dermatopathologists have emphasized the need for computational pathology (CPATH) systems. CPATH systems with deep learning (DL) models have the potential to identify the presence of melanoma by exploiting underlying morphological and cellular features. This paper proposes a DL method to detect melanoma and distinguish between normal skin and benign/malignant melanocytic lesions in Whole Slide Images (WSI). Our method detects lesions with high accuracy and localizes them on a WSI to identify potential regions of interest for pathologists. Interestingly, our DL method relies on using a single CNN network to create localization maps first and use them to perform slide-level predictions to determine patients who have melanoma. Our best model provides favorable patch-wise classification results with a 0.992 F1 score and 0.99 sensitivity on unseen data. The source code is https://github.com/RogerAmundsen/Melanoma-Diagnosis-and-Localization-from-Whole-Slide-Images-using-Convolutional-Neural-Networks.