Recent advances and clinical applications of deep learning in medical image analysis
Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …
processing algorithms, and deep learning based models have been remarkably successful …
Mitigating bias in radiology machine learning: 2. Model development
There are increasing concerns about the bias and fairness of artificial intelligence (AI)
models as they are put into clinical practice. Among the steps for implementing machine …
models as they are put into clinical practice. Among the steps for implementing machine …
Self-supervised pre-training of swin transformers for 3d medical image analysis
Abstract Vision Transformers (ViT) s have shown great performance in self-supervised
learning of global and local representations that can be transferred to downstream …
learning of global and local representations that can be transferred to downstream …
Clip-driven universal model for organ segmentation and tumor detection
An increasing number of public datasets have shown a marked impact on automated organ
segmentation and tumor detection. However, due to the small size and partially labeled …
segmentation and tumor detection. However, due to the small size and partially labeled …
Universeg: Universal medical image segmentation
While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …
[HTML][HTML] Knowledge-enhanced visual-language pre-training on chest radiology images
While multi-modal foundation models pre-trained on large-scale data have been successful
in natural language understanding and vision recognition, their use in medical domains is …
in natural language understanding and vision recognition, their use in medical domains is …
[HTML][HTML] The liver tumor segmentation benchmark (lits)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark
(LiTS), which was organized in conjunction with the IEEE International Symposium on …
(LiTS), which was organized in conjunction with the IEEE International Symposium on …
CaraNet: context axial reverse attention network for segmentation of small medical objects
Segmenting medical images accurately and reliably is important for disease diagnosis and
treatment. It is a challenging task because of the wide variety of objects' sizes, shapes, and …
treatment. It is a challenging task because of the wide variety of objects' sizes, shapes, and …
Dira: Discriminative, restorative, and adversarial learning for self-supervised medical image analysis
F Haghighi, MRH Taher… - Proceedings of the …, 2022 - openaccess.thecvf.com
Discriminative learning, restorative learning, and adversarial learning have proven
beneficial for self-supervised learning schemes in computer vision and medical imaging …
beneficial for self-supervised learning schemes in computer vision and medical imaging …
Label-free liver tumor segmentation
We demonstrate that AI models can accurately segment liver tumors without the need for
manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two …
manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two …