# Deep Convolutional Encoder-Decoders with Aggregated Multi-Resolution Skip Connections for Skin Lesion Segmentation

## TL;DR

This paper introduces a novel encoder-decoder neural network architecture with multi-resolution skip connections and pyramid pooling for improved skin lesion segmentation, achieving higher accuracy than existing methods.

## Contribution

The proposed model integrates pyramid pooling modules into deep skip connections, enhancing global context aggregation and spatial detail retention in skin lesion segmentation.

## Key findings

- Achieved a Jaccard index of 0.837 on ISIC 2018 dataset.
- Outperformed U-Net in segmentation accuracy.
- Demonstrated potential for clinical application.

## Abstract

The prevalence of skin melanoma is rapidly increasing as well as the recorded death cases of its patients. Automatic image segmentation tools play an important role in providing standardized computer-assisted analysis for skin melanoma patients. Current state-of-the-art segmentation methods are based on fully convolutional neural networks, which utilize an encoder-decoder approach. However, these methods produce coarse segmentation masks due to the loss of location information during the encoding layers. Inspired by Pyramid Scene Parsing Network (PSP-Net), we propose an encoder-decoder model that utilizes pyramid pooling modules in the deep skip connections which aggregate the global context and compensate for the lost spatial information. We trained and validated our approach using ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection grand challenge dataset. Our approach showed a validation accuracy with a Jaccard index of 0.837, which outperforms U-Net. We believe that with this reported reliable accuracy, this method can be introduced for clinical practice.