|
|
- Artificial Intelligence (AI) in Oncology: Current Landscape . . .
Multiple groups have now leveraged NLST imaging data to develop lung cancer detection models For example, Ardila et al 29 developed an approach to localize lung nodules and predict their likelihood of malignancy, where this model is now licensed by the AI company Aidence 30 In addition to the use of computed tomography (CT) exams, there have
- 10 Mind-Blowing AI Projects Transforming Medical Imaging
4 Google’s AI for Lung Cancer Detection Google has created an AI model that can detect lung cancer in CT scans with a higher accuracy rate than human radiologists In a study published in Nature Medicine, the AI demonstrated a 5% reduction in false positives and an 11% reduction in false negatives
- Generative AI Models and Capabilities in Cancer Medical . . .
Deep learning has emerged as an effective tool in the cancer diagnosis, promising enhanced accuracy, efficiency, and speed in various aspects of cancer assessment Artificial intelligence algorithms that use the deep learning method are effective at analyzing medical imaging like Magnetic Resonance Imaging (MRI), X-radiation images
- Using AI to Detect Cancer at an Early Stage: Transforming . . .
A Brief History of Cancer Detection Before modern medical imaging, cancer detection relied heavily on physical symptoms and biopsy procedures By the late 19 th and early 20 th centuries, X-rays and microscopy became essential tools for identifying abnormal growths However, misdiagnosis rates were high due to human limitations in analyzing
- Applying AI to cancer research, a UF professor helps . . .
Shao’s lab is called the Medical Imaging Research for Translational Healthcare with Artificial Intelligence Laboratory, or the MIRTH AI Lab One of the lab’s main projects involves using AI to improve prostate cancer diagnosis Prostate cancer is the most diagnosed cancer, excluding skin cancer, and the second leading cause of cancer death
- Using generative AI to investigate medical imagery models and . . .
Flowchart of our approach illustrating the four main steps, including (1) developing the ML classifier for a prediction task of interest; (2) developing the generative StylEx ML model to examine the frozen classifier; (3) generating visual attributes using the generative model and extracting the most influential visual attributes; and (4) involving an interdisciplinary panel to examine the
- Data infrastructures for AI in medical imaging: a report on . . .
The remainder of this paper is structured as follows First, we provide an overview of existing approaches to building such infrastructures for data storage, curation, and management for AI developments in cancer imaging, focusing on the data models used, on security aspects, and curation tools required from such infrastructures
|
|
英文每年常用名排名
2023 年排名
2022 年排名
2021 年排名
2020 年排名
2019 年排名
2018 年排名
2017 年排名
2016 年排名
2015 年排名
2014 年排名
2013 年排名
2012 年排名
2011 年排名
2010 年排名
2009 年排名
2008 年排名
2007 年排名
2006 年排名
2005 年排名
2004 年排名
2003 年排名
2002 年排名
2001 年排名
2000 年排名
英文名字起源
希伯来
希腊
条顿
印度
拉丁
拉丁语
古英语
英格兰
阿拉伯
法国
盖尔
英语
匈牙利
凯尔特
西班牙
居尔特
非洲
美洲土著
挪威
德国
威尔士
斯拉夫民族
古德语
爱尔兰
波斯
古法语
盎格鲁撒克逊
意大利
盖尔语
未知
夏威夷
中古英语
梵语
苏格兰
俄罗斯
土耳其
捷克
希腊;拉丁
斯干那维亚
瑞典
波兰
乌干达
拉丁;条顿
巴斯克语
亚拉姆
亚美尼亚
斯拉夫语
斯堪地纳维亚
越南
荷兰
|