DeDA: Deep Directed Accumulator

• URL: http://arxiv.org/abs/2303.08434v1
• Date: Wed, 15 Mar 2023 08:12:28 GMT
• Title: DeDA: Deep Directed Accumulator
• Authors: Hang Zhang, Rongguang Wang, Renjiu Hu, Jinwei Zhang, and Jiahao Li
• Abstract summary: Chronic active multiple sclerosis lesions, also termed as rim+ lesions, can be characterized by a hyperintense rim at the edge of the lesion on quantitative susceptibility maps. Recent studies have shown that the identification performance of such lesions remains unsatisfied due to the limited amount of data and high class imbalance. We propose a simple yet effective image processing operation, deep directed (DeDA) that provides a new perspective for injecting domain-specific inductive biases (priors) into neural networks for rim+ lesion identification.
• Score: 10.779418078441065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Chronic active multiple sclerosis lesions, also termed as rim+ lesions, can be characterized by a hyperintense rim at the edge of the lesion on quantitative susceptibility maps. These rim+ lesions exhibit a geometrically simple structure, where gradients at the lesion edge are radially oriented and a greater magnitude of gradients is observed in contrast to rim- (non rim+) lesions. However, recent studies have shown that the identification performance of such lesions remains unsatisfied due to the limited amount of data and high class imbalance. In this paper, we propose a simple yet effective image processing operation, deep directed accumulator (DeDA), that provides a new perspective for injecting domain-specific inductive biases (priors) into neural networks for rim+ lesion identification. Given a feature map and a set of sampling grids, DeDA creates and quantizes an accumulator space into finite intervals, and accumulates feature values accordingly. This DeDA operation is a generalized discrete Radon transform and can also be regarded as a symmetric operation to the grid sampling within the forward-backward neural network framework, the process of which is order-agnostic, and can be efficiently implemented with the native CUDA programming. Experimental results on a dataset with 177 rim+ and 3986 rim- lesions show that 10.1% of improvement in a partial (false positive rate<0.1) area under the receiver operating characteristic curve (pROC AUC) and 10.2% of improvement in an area under the precision recall curve (PR AUC) can be achieved respectively comparing to other state-of-the-art methods. The source code is available online at https://github.com/tinymilky/DeDA

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