MICROLINE INCBusiness Directories,Company Directories

Company Name: Corporate Name:	MICROLINE INC
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Company Address:	800 Cummings Ctr # 157x,BEVERLY,MA,USA
ZIP Code: Postal Code:	01915-6186
Telephone Number:	9789229209 (+1-978-922-9209)
Fax Number:	9789229810 (+1-978-922-9810)
Website:	www. microlineinc. com
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USA SIC Code(Standard Industrial Classification Code):	384104
USA SIC Description:	Physicians & Surgeons Equip & Supls-Mfrs
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MedCLIP：CLIP + 医学语义匹配策略，解决模型误将不同 . . .
MedCLIP 的目标是利用医学专业知识提高 CLIP 模型在医学领域的适用性和准确性，特别是在处理医学图像和相关文本数据时，如X光图和病历报告。这使得 MedCLIP 特别适用于如自动病理检测、医学教育和临床决策支持等任务。
EMNLP22 | MedCLIP: Contrastive Learning from Unpaired Medical Images . . .
About EMNLP'22 | MedCLIP: Contrastive Learning from Unpaired Medical Images and Texts
【论文阅读】biomedclip:一种针对生物医学领域的大规模 . . .
生物医学文本和图像与CLIP预训练中使用的网络数据截然不同。我们发现标准的CLIP设置对于生物医学视觉语言预训练是次优的。因此，我们对潜在的适应性进行了系统的研究，并确定了生物医学领域的一系列特定领域的适应性。
MedCLIP: Contrastive Learning from Unpaired Medical Images and Text
Existing vision-text contrastive learning like CLIP aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction
沈定刚团队新作：首篇医学领域中的CLIP技术综述 - 知乎
本文首次对医学影像中的 CLIP 范式进行全面调研，涵盖CLIP基础知识、CLIP预训练在医学领域的适应性、技术和应用，包括分类、密集预测和跨模态等任务。
【技术追踪】MedCLIP-SAM：桥接文本和图像实现通用医学 . . .
近年来，基础模型（foundation models）的出现，如 CLIP 和分割任意模型（SAM），具有全面的跨域表示，为交互式和通用的图像分割打开了大门。然而，对这些模型进行数据高效的医学图像分割的探索仍然有限。
MedCLIP README. md at main · RyanWangZf MedCLIP · GitHub
MedCLIP Wang, Zifeng and Wu, Zhenbang and Agarwal, Dinesh and Sun, Jimeng (2022) MedCLIP: Contrastive Learning from Unpaired Medical Images and Texts EMNLP'22 Paper PDF
ZiyueWang med-clip · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science
MedCLIP: Contrastive Learning from Unpaired Medical Images and Text
As evident, CLIP uses 400M image-text pairs in the training phase, which is not just com-putationally expensive but also infeasible in med-ical domain due to limited data
EMNLP22 | 医疗多模态预训练：如何利用非天然配对的医疗 . . .
MedCLIP要解决的问题：（1）如何解决只能利用配对图文训练的限制；（2）如何解决由于只使用配对图文作为正样本带来的假阴性样本问题。首先，跟CLIP相比，医疗领域的图像文本配对总量要小的多。 CLIP可以在4亿数据上充分训练，但是，X-ray和配对的报告的公开数据集最大的也只有数十万这个级别，分别是CheXpert（20万）和MIMIC-CXR（37万）。这就严重限制了模型的能力。同时，我们其实还有大量的纯医疗图像或者纯文本数据。由于使用CLIP的对比学习方法，模型只能利用天生配对的图片+报告来训练。