Foundation model for advancing healthcare: challenges, opportunities and future directions

Y He, F Huang, X Jiang, Y Nie, M Wang… - IEEE Reviews in …, 2024‏ - ieeexplore.ieee.org
Foundation model, trained on a diverse range of data and adaptable to a myriad of tasks, is
advancing healthcare. It fosters the development of healthcare artificial intelligence (AI) …

Usfm: A universal ultrasound foundation model generalized to tasks and organs towards label efficient image analysis

J Jiao, J Zhou, X Li, M **a, Y Huang, L Huang… - Medical Image …, 2024‏ - Elsevier
Inadequate generality across different organs and tasks constrains the application of
ultrasound (US) image analysis methods in smart healthcare. Building a universal US …

OpenMEDLab: An open-source platform for multi-modality foundation models in medicine

X Wang, X Zhang, G Wang, J He, Z Li, W Zhu… - arxiv preprint arxiv …, 2024‏ - arxiv.org
The emerging trend of advancing generalist artificial intelligence, such as GPTv4 and
Gemini, has reshaped the landscape of research (academia and industry) in machine …

[HTML][HTML] Integrating language into medical visual recognition and reasoning: A survey

Y Lu, A Wang - Medical Image Analysis, 2025‏ - Elsevier
Abstract Vision-Language Models (VLMs) are regarded as efficient paradigms that build a
bridge between visual perception and textual interpretation. For medical visual tasks, they …

Deblurring masked image modeling for ultrasound image analysis

Q Kang, Q Lao, J Gao, J Liu, H Yi, B Ma, X Zhang… - Medical Image …, 2024‏ - Elsevier
Recently, large pretrained vision foundation models based on masked image modeling
(MIM) have attracted unprecedented attention and achieved remarkable performance across …

Masked Image Modeling: A Survey

V Hondru, FA Croitoru, S Minaee, RT Ionescu… - arxiv preprint arxiv …, 2024‏ - arxiv.org
In this work, we survey recent studies on masked image modeling (MIM), an approach that
emerged as a powerful self-supervised learning technique in computer vision. The MIM task …

Breast tumor classification based on self-supervised contrastive learning from ultrasound videos

Y Tang, S Tang, J Zhang, H Chen - arxiv preprint arxiv:2408.10600, 2024‏ - arxiv.org
Background: Breast ultrasound is prominently used in diagnosing breast tumors. At present,
many automatic systems based on deep learning have been developed to help radiologists …

A novel open-source ultrasound dataset with deep learning benchmarks for spinal cord injury localization and anatomical segmentation

A Kumar, K Kotkar, K Jiang, M Bhimreddy… - arxiv preprint arxiv …, 2024‏ - arxiv.org
While deep learning has catalyzed breakthroughs across numerous domains, its broader
adoption in clinical settings is inhibited by the costly and time-intensive nature of data …

[PDF][PDF] Ultramae: Multi-modal masked autoencoder for ultrasound pre-training

A Rahman, VM Patel - Medical Imaging with Deep …, 2024‏ - raw.githubusercontent.com
Pre-training on a large dataset such as ImageNet followed by supervised fine-tuning has
brought success in various deep learning-based tasks. However, the modalities of natural …

:~Cataract Surgical Masked Autoencoder (MAE) based Pre-training

NA Shah, WGC Bandara, S Skider, SS Vedula… - arxiv preprint arxiv …, 2025‏ - arxiv.org
Automated analysis of surgical videos is crucial for improving surgical training, workflow
optimization, and postoperative assessment. We introduce a CSMAE, Masked Autoencoder …