T-AutoML: Automated Machine Learning for Lesion Segmentation using Transformers in 3D Medical Imaging
This addresses the need for automated and efficient lesion segmentation in clinical research, though it is incremental as it builds on existing AutoML and transformer approaches.
The paper tackles the problem of manual design in deep learning for lesion segmentation in 3D medical imaging by proposing T-AutoML, an automated method that searches for neural architectures, hyper-parameters, and data augmentation strategies simultaneously, achieving state-of-the-art performance on large-scale public datasets.
Lesion segmentation in medical imaging has been an important topic in clinical research. Researchers have proposed various detection and segmentation algorithms to address this task. Recently, deep learning-based approaches have significantly improved the performance over conventional methods. However, most state-of-the-art deep learning methods require the manual design of multiple network components and training strategies. In this paper, we propose a new automated machine learning algorithm, T-AutoML, which not only searches for the best neural architecture, but also finds the best combination of hyper-parameters and data augmentation strategies simultaneously. The proposed method utilizes the modern transformer model, which is introduced to adapt to the dynamic length of the search space embedding and can significantly improve the ability of the search. We validate T-AutoML on several large-scale public lesion segmentation data-sets and achieve state-of-the-art performance.