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Gastric Histopathology Image Classification by Transformer, GasHis-Transformer

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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).


Can Artificial intelligence Affect Everything in Our Society?

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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.


Pastry scanning Artificial Intelligence Machine can Detect Cancer - MEDizzy Journal

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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.


Techniques for Dimensionality Reduction

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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.


Detecting Breast Cancer Using Machine Learning

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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.


Architectures for Medical Image Segmentation [Part 2: Attention UNet]

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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.


Fetch.ai To Develop AI-Powered Collective Learning Module For Cancer Cell Detection

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This partnership will allow PSNC to use and contribute data to train algorithms that can be used by hospitals and research centers worldwide to identify and detect circulating cancer cells in patients' blood or tissue biopsies in the upcoming future. Collective Learning Module, distributed parties can work together to train machine learning models using blockchain technology and AI learning capabilities without sharing the underlying data or trusting any of the individual participants. It was most recently deployed to identify COVID-19 cases using chest X-ray images establishing a clear distinction between COVID-19 versus pneumonia cases. The collective learning protocol successfully distinguished COVID-19 patients from those with pneumonia from different causes with an accuracy of 97%. As a part of this initiative, Fetch.ai's


Artificial Intelligence in Radiology: Current Applications and Future Technologies

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For lung nodules, CNN have been shown to distinguish between benign and malignant classifications at a higher performance than traditional CADx systems due to their ability to function at higher degrees of noise tolerance (Hosny et al. 2018; Nasrullah et al. 2019). Furthermore, in a study done on patients with non-small cell lung cancer, AI CADx algorithms were able to use CT images to significantly predict which cancers contained EGFR mutations, informing on potential treatment with Gefitinib (Bi et al. 2019). Deep learning algorithms have also been trained to accurately classify prostate cancer on Magnetic Resonance Imaging (MRI), which can promote early treatment as well as decrease the number of unnecessary prostate biopsies and prostatectomy procedures performed (Bi et al. 2019). An additional study reported an AI system that was able to use MRI imaging to accurately generate brain tumour classification differentials at a level that exceeded human performance. The algorithm generated the correct diagnosis in one of its top three differentials 91% of the time, outperforming academic neuroradiologists (86%), fellows (77%), general radiologists (57%), and radiology residents (56%) (Rauschecker et al. 2020).


Artificial Intelligence helps Doctors in the fight against Breast Cancer

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… Artificial Technology can help women get a more accurate Mammogram. Dr. Hicks says, "AI is helping Doctors in the fights against Breast Cancer.".


£36 million boost for AI technologies to revolutionise NHS care

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"AI has the potential to completely revolutionise every part of how we approach healthcare, from how we diagnose diseases and the speed at which our doctors and nurses deliver treatments to how we support people's mental health. "The 38 projects we are backing reflect the UK's trailblazing approach to innovation in the healthcare sector, and could help us take a leap forward in the quality of care and the speed of disease diagnoses and treatment in the NHS. "Confronted with this global pandemic, our tech sector has risen to the challenge and upended how we do things through innovations to support people to test from home, complete remote consultations and diagnose issues safely." "Precision cancer diagnosis, accurate surgery, and new ways of offering mental health support are just a few of the promising real world patient benefits. Because as the NHS comes through the pandemic, rather than a return to old ways, we're supercharging a more innovative future. "So today our message to developers worldwide is clear - the NHS is ready to help you test your innovations and ensure our patients are among the first in the world to benefit from new AI technologies." The AI in Health and Care Award aims to accelerate the testing and evaluation of AI in the NHS so patients can benefit from faster and more personalised diagnosis and greater efficiency in screening services. For example, use of Paige Prostate will be able to give more information about prostate cancer, including detecting a tumour, its size and how severe it is, enabling clinicians to make treatment more specific and more targeted. As well as this, Mia by Kheiron Medical, a winner from the first round of the AI Awards, aims to replace the need for two radiologists to review breast cancer scans by instead using one radiologist and the AI, making the process faster and more efficient. "These trials are making the AI revolution a reality for patients.