Transformers in medical imaging: A survey

F Shamshad, S Khan, SW Zamir, MH Khan… - Medical image …, 2023 - Elsevier
Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Artificial intelligence in CT and MR imaging for oncological applications

R Paudyal, AD Shah, O Akin, RKG Do, AS Konar… - Cancers, 2023 - mdpi.com
Simple Summary The two most common cross-sectional imaging modalities, computed
tomography (CT) and magnetic resonance imaging (MRI), have shown enormous utility in …

PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation

G Wang, X Luo, R Gu, S Yang, Y Qu, S Zhai… - Computer methods and …, 2023 - Elsevier
Abstract Background and Objective: Open-source deep learning toolkits are one of the
driving forces for develo** medical image segmentation models that are essential for …

Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors

J Peng, DD Kim, JB Patel, X Zeng, J Huang… - Neuro …, 2022 - academic.oup.com
Background Longitudinal measurement of tumor burden with magnetic resonance imaging
(MRI) is an essential component of response assessment in pediatric brain tumors. We …

GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows

S Pati, SP Thakur, İE Hamamcı, U Baid… - Communications …, 2023 - nature.com
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and
clinical communities. However, greater expertise is required to develop DL algorithms, and …