If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
De-Sheng Chen,* Tong-Fu Wang,* Jia-Wang Zhu, Bo Zhu, Zeng-Liang Wang, Jian-Gang Cao, Cai-Hong Feng, Jun-Wei Zhao Department of Sports Medicine and Arthroscopy, Tianjin Hospital of Tianjin University, Tianjin, People's Republic of China *These authors contributed equally to this work Correspondence: Jia-Wang Zhu Department of Sports Medicine and Arthroscopy, Tianjin Hospital of Tianjin University, Tianjin, People's Republic of China Email [email protected] Purpose: We aim to present an unsupervised machine learning application in anterior cruciate ligament (ACL) rupture and evaluate whether supervised machine learning-derived radiomics features enable prediction of ACL rupture accurately. Patients and Methods: Sixty-eight patients were reviewed. Their demographic features were recorded, radiomics features were extracted, and the input dataset was defined as a collection of demographic features and radiomics features. The input dataset was automatically classified by the unsupervised machine learning algorithm. Then, we used a supervised machine learning algorithm to construct a radiomics model.
Leaders from Europe and the US convened to explore exciting leaps forward of AI in oncology at the HIMSS21 & Health 2.0 European Health Conference, though the panel highlighted key barriers to greater acceptance and adoption of AI into mainstream care. The'New Frontiers of AI and Data Analytics in Oncology' session, moderated by Professor Karol Sikora, chief medical officer, Rutherford Health and former chief of the Cancer Programme, WHO, also saw leaders share innovative applications for AI used across the cancer pathway. The panel of experts also included Professor Barbara Alicja Jereczek-Fossa, associate professor of Radiation Oncology, University of Milan and head of Radiotherapy Division, European Institute of Oncology, and her colleague, Eng. Joining from the US was Dr Tufia Haddad, chair of Digital Health, Department of Oncology, Mayo Clinic and chair of Practice Innovation and Platform, Mayo Clinic Cancer Center. While AI is already widely used in oncology in image analysis and other areas, exciting new applications are being trialled at leading cancer centres across the globe.
Back then engineering was all about blueprints, sketches, and physical models. But today it is intensively about software tools and computer designs. The demand for artificial intelligence and digital technology has been gaining momentum. Advancements in the AI sector are transforming smart systems and supervised machine learning to a great extent. Artificial intelligence systems will ease the laborious tasks that engineers do such as finding relevant content, fixing errors, and determining solutions.
GasHis-Transformer is a model for realizing gastric histopathological image classification (GHIC), which automatically classifies microscopic images of the stomach into normal and abnormal cases in gastric cancer diagnosis, as shown in the figure. GasHis-Transformer is a multi-scale image classification model that combines the best features of Vision Transformer (ViT) and CNN, where ViT is good for global information and CNN is good for local information. GasHis-Transformer consists of two important modules, Global Information Module ( GIM) and Local Information Module ( LIM), as shown in the figure below. GasHisTransformer has high classification performance on the test data of gastric histopathology dataset, with estimate precision, recall, F1-score, and accuracy of 98.0%, 100.0%, 96.0%, and 98.0%, respectively. GasHisTransformer consists of two modules: Global Information Module (GIM) and Local Information Module (LIM).
AI, or artificial intelligence, has become huge in recent years and has affected many aspects of our society. We've seen it in our restaurants, our hospitals, and even our schools. AI is even impacting the latest casino bonuses. Since many people like to gamble online, online casinos have implemented AI to help you beat probabilities and earn you more bonuses. However, you sometimes have to wonder whether AI is affecting your life as well.
Scanning a tissue sample for cancer cells is a painstakingly time consuming process. A pathologist has to look over the sample slide in a microscope, checking each cell to see if there is an abnormality. However, a surprising Japanese artificial intelligence (AI) machine called BakeryScan that identifies bakery items has come to the rescue. According to an article reported in The New Yorker, a doctor once walked into a Tokyo bakery in 2019. There, he saw a multitude of pastry items he could choose from and got excited. But it was the checkout process that impressed him the most.
In addition to this, the recent'Big Bang' in large datasets across companies, organisation, and government departments has resulted in a large uptake in data mining techniques. So, what is data mining? Simply put, it's the process of discovering trends and insights in high-dimensionality datasets (those with thousands of columns). On the one hand, the high-dimensionality datasets have enabled organisations to solve complex, real-world problems, such as reducing cancer patient waiting time, predicting protein structure associated with COVID-19, and analysing MEG brain imaging scans. However, on the other hand, large datasets can sometimes contain columns with poor-quality data, which can lower the performance of the model -- more isn't always better.
I remember sitting in my 8th grade English class as we were all going around one day, naming a family member for whom we were grateful. I remember the boy who raised his hand shyly and shared that he was grateful for his mom, who had been battling breast cancer. After he shared his mother's story, multiple other people shared the name of their mothers, aunts, grandmothers, and close family members that had breast cancer. It was an eye-opening experience. Until then, I had not realized how prevalent breast cancer was.
I started writing about network architectures useful for medical image segmentation i.e. In the first article, I had covered basic UNet and 3D UNet. You can find that here. In this article, I'm going to go over Attention UNet. Fully convolutional neural networks (FCNNs) like UNet outperform traditional approaches in medical image analysis.