Machine learning methods for small data challenges in molecular science
B Dou, Z Zhu, E Merkurjev, L Ke, L Chen… - Chemical …, 2023 - ACS Publications
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
Transformers in medical imaging: A survey
Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …
successfully applied to several computer vision problems, achieving state-of-the-art results …
[HTML][HTML] TotalSegmentator: robust segmentation of 104 anatomic structures in CT images
J Wasserthal, HC Breit, MT Meyer… - Radiology: Artificial …, 2023 - ncbi.nlm.nih.gov
Purpose To present a deep learning segmentation model that can automatically and
robustly segment all major anatomic structures on body CT images. Materials and Methods …
robustly segment all major anatomic structures on body CT images. Materials and Methods …
Transformers in medical image segmentation: A review
H Xiao, L Li, Q Liu, X Zhu, Q Zhang - Biomedical Signal Processing and …, 2023 - Elsevier
Abstract Background and Objectives: Transformer is a model relying entirely on self-
attention which has a wide range of applications in the field of natural language processing …
attention which has a wide range of applications in the field of natural language processing …
SwinBTS: A method for 3D multimodal brain tumor segmentation using swin transformer
Brain tumor semantic segmentation is a critical medical image processing work, which aids
clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural …
clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural …
Deep learning-enabled medical computer vision
A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the
potential for many fields—including medicine—to benefit from the insights that AI techniques …
potential for many fields—including medicine—to benefit from the insights that AI techniques …
Max-deeplab: End-to-end panoptic segmentation with mask transformers
Abstract We present MaX-DeepLab, the first end-to-end model for panoptic segmentation.
Our approach simplifies the current pipeline that depends heavily on surrogate sub-tasks …
Our approach simplifies the current pipeline that depends heavily on surrogate sub-tasks …
[HTML][HTML] Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation
Automatic segmentation methods are an important advancement in medical image analysis.
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
Medical image segmentation using deep learning: A survey
Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …
papers has been presented recording the success of deep learning in the field. A …
Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19
The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world.
Medical imaging such as X-ray and computed tomography (CT) plays an essential role in …
Medical imaging such as X-ray and computed tomography (CT) plays an essential role in …