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Articles by Linshan Wu
An explainable biomedical foundation model via large-scale concept-enhanced vision–language pretraining
Abstract Artificial intelligence for medical imaging is required to be accurate and interpretable to clinicians. However, current multimodal biomedical foundation models often prioritize performance over explainability. Here we present ConceptCLIP, an explainable biomedical foundation model that achieves state-of-the-art diagnostic accuracy while delivering human-interpretable explanations across diverse imaging modalities.
A universal foundation model for grounded biomedical image interpretation
Abstract The integration of AI-assisted biomedical image analysis into clinical practice demands AI-generated findings that are not only accurate but also interpretable. However, existing models generally lack the ability to simultaneously generate diagnostic findings and localize corresponding targets. This limitation makes it challenging to correlate AI-generated findings with visual evidence and interpret the results.
Towards generalizable AI in medicine via Generalist–Specialist Collaboration
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Abstract Generalist foundation models (GFMs) are renowned for their exceptional capability and flexibility in diverse tasks. In the field of medicine, while GFMs exhibit superior generalizability, specialist models excel in precision because of their domain-specific knowledge. Here we show a cooperative framework, Generalist–Specialist Collaboration (GSCo), that synergistically combines a powerful generalist model with lightweight specialists.
By Sunan He, Yuxiang Nie, Hongmei Wang, Shu Yang, Yihui Wang, Zhiyuan Cai, Zhixuan Chen, Yingxue Xu, Linshan Wu, Luyang Luo, Huiling Xiang, Xi Lin, Yifan Peng, George Shih, Ziyang Xu, Xian Xin Wu, Qiong Wang, Varut Vardhanabhuti, Xiaohui Duan, Yefeng Zheng, Pranav Rajpurkar, Kang Zhang, Hao Chen, Mingxiang Wu
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Nature
Verified
Generative AI for misalignment-resistant virtual staining to accelerate histopathology workflows
Abstract Accurate histopathological diagnosis typically relies on multiple chemical stains, a process that is labor-intensive, tissue-consuming, and environmentally taxing. While virtual staining offers a faster, tissue-conserving alternative, its clinical adoption is hindered by the requirement for perfectly aligned paired data, which is difficult to obtain due to tissue distortion during chemical processing.
[2602.12317] Free Lunch in Medical Image Foundation Model Pre-training via Randomized Synthesis and Disentanglement
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Bioinspired MXene‐Polymer Actuators with “Two‐Way Traffic” and Positive Feedback Loops
Conflicts of Interest The authors declare no conflict of interest. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request. Supporting Information Filename Description adfm73637-sup-0013-SuppMat.docx2.1 MB Supporting file: adfm73637-sup-0013-SuppMat.docx. adfm73637-sup-0001-MovieS1.mp46.8 MB Supporting file: adfm73637-sup-0001-MovieS1.mp4.
Study on Single Event Effects of Enhanced GaN HEMT Devices under Various Conditions
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Droplet Manipulation on Lubricant Self-Mediating Slippery PDMS Films
Further exploration is needed for sustainable and precise droplet manipulation on intelligent surfaces, especially the problem of SLIPS failure caused by lubricant loss. In this work, a self-mediating photothermal lubrication surface was designed. Through a simple preparation method, it was possible to generate a new lubrication layer through near-infrared light (NIL) and perform sustainable and precise droplet manipulation even after the surface lubricant was consumed.
Biomimetic multifunctional silver @ titanium dioxide composite cloth for oil and water separation
Biomimetic multifunctional silver @ titanium dioxide composite cloth for oil and water separation Abstract Herein, a multifunctional silver @ titanium dioxide composite cloth (MSTCC) with convertible wettability was prepared in a uncomplex method. Due to the unique properties of nano-titania and nano-silver, MSTCC can achieve reversible wettability transition in IR or UV irradiation. This switchable wettability of MSTCC was be used to separate water-heavy oil mixtures or water-light oil mixtures.
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