Extracting architectural patterns of deep neural networks for disease detection: Student research abstract

• Type: Article in International Conference Proceedings Book
• Date: 2020

Authors

Mafalda Falcão Ferreira (Author) FEUP

Conference proceedings International

Title: Proceedings of the 35th Annual ACM Symposium on Applied Computing

Pages: 1150-1153

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Other information

Authenticus ID: P-00R-YWD

DOI: 10.1145/3341105.3374224

Abstract (EN): The importance of early detection of diseases with high-mortality is crucial to save lives. Deep Learning algorithms are recurrently used by many researchers that aim to model the progression and treatment of these conditions. There is growing evidence that the complexity of a Deep Learning model is correlated to its performance: the deeper the network, the more accurate it is. However, as the topology deepens, training gets more demanding: (1) increased need of data, (2) increased computational costs, and (3) increased time for evaluation, fine-tuning, and subsequent feedback-based activities inherent to Data Science, with direct impact on the exploration towards finding the best model, due to an inherent trial-and-error approach. We hypothesize that there exist (domain-specific) architectural patterns that, if applied during the model exploration phase, allow an overall improvement of the training performance. Should it be true, it would significantly reduce the exploration phase length, contributing to both Medicine and Computer Science fields. © 2020 Owner/Author.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4