# Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research

arXiv: https://arxiv.org/abs/1909.00311 · HTML: https://mlsanity.com/paper/1909.00311/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2019.

## Built on by (most cited first, 10 of 10)

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- [Machine Learning and Transformers for Thyroid Carcinoma Diagnosis: A Review](https://mlsanity.com/paper/2403.13843/) (2024)
- [Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives](https://mlsanity.com/paper/2309.14936/) (2023)
- [SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems](https://mlsanity.com/paper/2212.03410/) (2022)
- [Ovarian Cancer Data Analysis using Deep Learning: A Systematic Review from the Perspectives of Key Features of Data Analysis and AI Assurance](https://mlsanity.com/paper/2311.11932/) (2023)
- [Resource-Adaptive Successive Doubling for Hyperparameter Optimization with Large Datasets on High-Performance Computing Systems](https://mlsanity.com/paper/2412.02729/) (2024)

## Abstract (arXiv)

Cancer is a complex disease, the understanding and treatment of which are being aided through increases in the volume of collected data and in the scale of deployed computing power. Consequently, there is a growing need for the development of data-driven and, in particular, deep learning methods for various tasks such as cancer diagnosis, detection, prognosis, and prediction. Despite recent successes, however, designing high-performing deep learning models for nonimage and nontext cancer data is a time-consuming, trial-and-error, manual task that requires both cancer domain and deep learning expertise. To that end, we develop a reinforcement-learning-based neural architecture search to automate deep-learning-based predictive model development for a class of representative cancer data. We develop custom building blocks that allow domain experts to incorporate the cancer-data-specific characteristics. We show that our approach discovers deep neural network architectures that have significantly fewer trainable parameters, shorter training time, and accuracy similar to or higher than those of manually designed architectures. We study and demonstrate the scalability of our approach on up to 1,024 Intel Knights Landing nodes of the Theta supercomputer at the Argonne Leadership Computing Facility.