On the challenges and perspectives of foundation models for medical image analysis
This article discusses the opportunities, applications and future directions of large-scale
pretrained models, ie, foundation models, which promise to significantly improve the …
pretrained models, ie, foundation models, which promise to significantly improve the …
U-mamba: Enhancing long-range dependency for biomedical image segmentation
Convolutional Neural Networks (CNNs) and Transformers have been the most popular
architectures for biomedical image segmentation, but both of them have limited ability to …
architectures for biomedical image segmentation, but both of them have limited ability to …
Swin-umamba: Mamba-based unet with imagenet-based pretraining
Accurate medical image segmentation demands the integration of multi-scale information,
spanning from local features to global dependencies. However, it is challenging for existing …
spanning from local features to global dependencies. However, it is challenging for existing …
Abdomenatlas-8k: Annotating 8,000 CT volumes for multi-organ segmentation in three weeks
Annotating medical images, particularly for organ segmentation, is laborious and time-
consuming. For example, annotating an abdominal organ requires an estimated rate of 30 …
consuming. For example, annotating an abdominal organ requires an estimated rate of 30 …
Foundation model for advancing healthcare: Challenges, opportunities, and future directions
Foundation model, which is pre-trained on broad data and is able to adapt to a wide range
of tasks, is advancing healthcare. It promotes the development of healthcare artificial …
of tasks, is advancing healthcare. It promotes the development of healthcare artificial …
Versatile medical image segmentation learned from multi-source datasets via model self-disambiguation
A versatile medical image segmentation model applicable to images acquired with diverse
equipment and protocols can facilitate model deployment and maintenance. However …
equipment and protocols can facilitate model deployment and maintenance. However …
Large window-based mamba unet for medical image segmentation: Beyond convolution and self-attention
In clinical practice, medical image segmentation provides useful information on the contours
and dimensions of target organs or tissues, facilitating improved diagnosis, analysis, and …
and dimensions of target organs or tissues, facilitating improved diagnosis, analysis, and …
Sa-med2d-20m dataset: Segment anything in 2d medical imaging with 20 million masks
Segment Anything Model (SAM) has achieved impressive results for natural image
segmentation with input prompts such as points and bounding boxes. Its success largely …
segmentation with input prompts such as points and bounding boxes. Its success largely …
AbdomenAtlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking
We introduce the largest abdominal CT dataset (termed AbdomenAtlas) of 20,460 three-
dimensional CT volumes sourced from 112 hospitals across diverse populations …
dimensional CT volumes sourced from 112 hospitals across diverse populations …
Gmai-mmbench: A comprehensive multimodal evaluation benchmark towards general medical ai
Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as
imaging, text, and physiological signals, and can be applied in various fields. In the medical …
imaging, text, and physiological signals, and can be applied in various fields. In the medical …