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'Outside-the-box' method of teaching AI models opens the prospect of finding new cancer treatments


A new'outside-the-box' method of teaching artificial intelligence (AI) models to make decisions could provide hope for finding new therapeutic methods for cancer, according to a new study from the University of Surrey. Computer scientists from Surrey have demonstrated that an open ended - or model-free - deep reinforcement learning method is able to stabilize large datasets (of up to 200 nodes) used in AI models. The approach holds open the prospect of uncovering ways to arrest the development of cancer by predicting the response of cancerous cells to perturbations including drug treatment. There are a heart-breaking number of aggressive cancers out there with little to no information on where they come from, let alone how to categorize their behavior. This is where machine learning can Dr Sotiris Moschoyiannis, corresponding author of the study from the University of Surreyprovide real hope for us all.

Lunit's AI boosts Guardant Health's cancer detection capability


The Lunit SCOPE PD-L1 does AI-based quantification of tissue samples using a scoring system. Integrating it with the Guardant test will enhance biomarker assessment for NSCLC patients, the company claimed in a press statement. The AI has helped lift the test's detection of PD-L1 by over 20% compared to manual pathologist interpretation.

Tricorder Tech: Automated Detection Of Isolated Single Cells Using Microscope Images And AI - Astrobiology


Research team leader Moeto Nagai explains: "We wanted to apply AI to the detection of single cells. As I had been performing mostly experiment-based research, the use of experimental data for AI research seemed to me to be a significant obstacle. However, the participation of graduate student Tanmay Debnath, the lead author of our study, who has experience in the research and development of AI technology, meant that we could rapidly make use of AI and ultimately led to the success of our development." The single-cell isolation and detection developed by this research can also be used to automatically monitor the activities of single cells. This research achieves accurate and highly reliable automated cell detection, while reducing human labor. Future applications for single-cell analysis include medical engineering applications in a wide range of areas such as cancer diagnosis, immune response, and drug discovery screening, which will contribute to the discovery of new treatment methods.

From Vaccines To AI: New Weapons In The Fight Against Cancer

International Business Times

Could humanity finally be gaining the upper hand in our age-old fight against cancer? Recent scientific and medical advances have added several new weapons to our arsenal, including personalised gene therapy, artificial intelligence screening, simple blood tests -- and potentially soon vaccines. Cancer accounted for nearly 10 million deaths -- almost one in six of the global total -- in 2020, according to the World Health Organization. Ahead of World Cancer Day on Saturday, here are some of the promising recent developments in diagnosing and treating the disease. Immunotherapy drugs, which stimulate the immune system to track down and kill cancerous cells, have been one the biggest advances in cancer treatment over the last decade.

Summer Internship, Data Analyst Intern


Find open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general, filtered by job title or popular skill, toolset and products used.

Ultra-processed food consumption linked to higher risk of death from ovarian, breast cancers: new study

FOX News

Fox News Flash top headlines are here. Check out what's clicking on Ultra-processed foods include those that undergo extensive processing during production. Food such as packaged chips, breakfast cereals, many frozen meals, carbonated drinks, cold cuts, hot dogs, candy and more usually fall into this category. The study from the U.K. tracked the diets of 200,000 adults between the ages of 40 and 69 over a decade, using the NOVA food classification system to determine the level of food processing.

This software tries to spot lung cancer years earlier. Can it?

Washington Post - Technology News

The finding, announced in late January by a team of researchers at Harvard's Massachusetts General Hospital and the Massachusetts Institute of Technology, is part of a growing medical trend of using algorithms to predict everything from breast cancer and prostate cancer to the likelihood of tumors regrowing. Though research is increasing, scientists say more testing needs to be done before fully unleashing these products into clinical settings.

Bringing Artificial Intelligence to Precision Medicine - Digital Salutem


Precision medicine is an emerging field of healthcare that aims to provide tailored treatments to patients based on their individual characteristics. This personalized approach has the potential to revolutionize the way we treat disease, and many believe that artificial intelligence (AI) will play a key role in its development. AI is already being used to analyze massive amounts of data and make predictions about a patient's health. While there are still many challenges ahead, researchers are confident that precision medicine will be more accessible as AI improves. Precision medicine is a medical model that focuses on providing personalized, patient-centered care.

Convolutional Neural Network for Breast Cancer Classification


Click here to read the full story with my Friend Link! Breast cancer is the second most common cancer in women and men worldwide. In 2012, it represented about 12 percent of all new cancer cases and 25 percent of all cancers in women. Breast cancer starts when cells in the breast begin to grow out of control. These cells usually form a tumor that can often be seen on an x-ray or felt as a lump. The tumor is malignant (cancer) if the cells can grow into (invade) surrounding tissues or spread (metastasize) to distant areas of the body.

CT Study Says Deep Learning Model Could Help Differentiate Between Acute Diverticulitis and Colon Carcinoma


Noting that overlapping imaging features on contrast-enhanced computed tomography (CT) can make it challenging to differentiate between acute diverticulitis and colon cancer, researchers say an emerging deep learning model may provide enhanced sensitivity and specificity for these conditions. In a retrospective study recently published in JAMA Network Open, researchers developed and tested a three-dimensional (3D) convolutional neural network (CNN) for 585 patients (mean age of 63.2) who underwent surgery for colon cancer or acute diverticulitis between July 1, 2005 and October 1, 2020, had venous phase CT imaging within 60 days prior to surgery and had segmental wall thickening in the colon that was independent of disease stage. In comparison to mean sensitivity and specificity rates of 77.6 percent and 81.6 percent, respectively, for radiologist readers, the study authors noted an 83.3 percent sensitivity rate and an 86.6 percent specificity rate for the 3D CNN model. The combination of the deep learning model and radiologist assessment resulted in an eight percent increase in sensitivity (85.6 percent) and a 9.7 percent increase in specificity (91.3 percent) over radiologist assessments, according to the study findings. The study authors also noted the reduction of false-negative rates with the 3D CNN model.