Kidney Tumor Detection using Attention based U-Net

Authors
Rathnayaka, Prabod
Jayasundara, Vinoj
Nawaratne, Rashmika
De Silva, Daswin
Ranasinghe, Weranja
Alahakoon, Damminda

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Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
The advancement of deep learning techniques has provoked the potential of using Medical Image Analysis (MIA) for disease detection and prediction in numerous ways. This has been mostly useful in identifying tumours and abnormalities in many organs of the human body. Particularly in kidney diseases, the treatment options such as surgery have largely benefitted by the ability to detect tumours in early stages, thereby shifting towards more efficient methods including conservative nephron procedures. Therefore, to enable the early detection of kidney tumours, we propose a convolutional neural network based U-Net architecture which is able to detect tumours using an attention mechanism. The proposed architecture was evaluated using KiTS19 Challenge dataset that includes a collection of multi-phase CT imaging, segmentation masks, and comprehensive clinical outcomes for 300 patients who underwent nephrectomy for kidney tumours. The outcomes demonstrate the ability of the proposed architecture to distinguish images with tumours in the kidney and support early tumour detection.

Identifiers
doi: 10.24926/548719.079