Opportunities and Challenges for Deep Learning in Brain Lesions

Jay B. Patel, Ken Chang, Syed Rakin Ahmed, Ikbeom Jang, Jayashree Kalpathy-Cramer

Abstract: In recent years, deep learning techniques have shown potential for incorporation in many facets of the medical imaging pipeline, from image acquisition/reconstruction to segmentation/classification to outcome prediction. Specifically, these models can help improve the efficiency and accuracy of image interpretation and quantification. However, it is important to note the challenges of working with medical imaging data, and how this can affect the effectiveness of the algorithms when deployed. In this review, we first present an overview of the medical imaging pipeline and some of the areas where deep learning has been used to improve upon the current standard of care for brain lesions. We conclude with a section on some of the current challenges and hurdles facing neuroimaging researchers.

External IDs:dblp:conf/brainles-ws/PatelCAJK21