# oncology

### The Hunt for Explainable AI

The notion that we should understand how artificial intelligences make decisions is gaining increasing currency. As we face a future in which important decisions affecting the course of our lives may be made by artificial intelligence (AI), the idea that we should understand how AIs make decisions is gaining increasing currency. Which hill to position a 20-year-old soldier on, who gets (or does not get) a home mortgage, which treatment a cancer patient receives … such decisions, and many more, already are being made based on an often unverifiable technology. "The problem is that not all AI approaches are created equal," says Jeff Nicholson, a vice president at Pega Systems Inc., makers of AI-based Customer Relationship Management (CRM) software. "Certain'black box' approaches to AI are opaque and simply cannot be explained."

### Accelerating Cancer Research with Deep Learning – Oak Ridge Leadership Computing Facility

A representation of a deep learning neural network designed to intelligently extract text-based information from cancer pathology reports. Despite steady progress in detection and treatment in recent decades, cancer remains the second leading cause of death in the United States, cutting short the lives of approximately 500,000 people each year. To better understand and combat this disease, medical researchers rely on cancer registry programs--a national network of organizations that systematically collect demographic and clinical information related to the diagnosis, treatment, and history of cancer incidence in the United States. The surveillance effort, coordinated by the National Cancer Institute (NCI) and the Centers for Disease Control and Prevention, enables researchers and clinicians to monitor cancer cases at the national, state, and local levels. Much of this data is drawn from electronic, text-based clinical reports that must be manually curated--a time-intensive process--before it can be used in research.

### Smartphone app uses selfies to detect Pancreatic Cancer

Better screening tools for all Cancer would only be a good thing, but one type in particular where it could have a huge impact is Pancreatic cancer. The symptoms of this disease don't often reveal themselves until it is well progressed, and it carries a five year survival rate of just a measly 9 percent, but scientists have now developed what could prove an exciting new diagnostics tool, in the form of a smartphone app that scans the white part of the eye for one of the deadly disease's early tell tale signs. Because it is so difficult to detect, sufferers of pancreatic cancer are often diagnosed well after the disease has already spread, and this means surgical removal of the tumour, the only potentially curative treatment, isn't possible. One of the early symptoms of pancreatic cancer is jaundice which is characterised by yellowing of the skin and eyes as a result of a substance called Bilirubin in the blood. But the trouble with Bilirubin build up, other than the fact it can be indicative of a number of diseases, is that it can only be picked up by a blood test that physicians won't administer unless there is already cause for concern.

### Google's Augmented Reality Microscope Quickly Highlights Cancer Cells

Discovering and diagnosing cancer may have just become much easier thanks to augmented reality. Google Research this week revealed an AR microscope (ARM) capable of detecting cancerous cells in real-time with the help of machine learning. Locating cancer with a standard microscope is a difficult and time-consuming process, with a raft of information for doctors to study and investigate. With this new solution, though, the microscope is able to quickly locate cancerous cells and then highlight them as a doctor peers inside. The platform uses a modified light microscope integrated with image analysis and machine learning algorithms into its field of view.

### [1804.07098] Unsupervised Prostate Cancer Detection on H&E using Convolutional Adversarial Autoencoders

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

### Google AR microscope uses machine learning to quickly spot cancer cells

Discovering and diagnosing cancer may have just become much easier thanks to augmented reality. Google Research this week revealed an AR microscope (ARM) capable of detecting cancerous cells in real-time with the help of machine learning. Locating cancer with a standard microscope is a difficult and time-consuming process, with a raft of information for doctors to study and investigate. With this new solution, though, the microscope is able to quickly locate cancerous cells and then highlight them as a doctor peers inside. The platform uses a modified light microscope integrated with image analysis and machine learning algorithms into its field of view.

### Tomorrow Edition - Interview with Mathematician, Medical Scientist, and Quantum Computation Specialist Dr. Joseph Geraci

Dr. Geraci's efforts mainly concern precision medicine, using mathematical and computational methods to construct models of disease that go beyond classical top-down clinical definitions. After completing postdocs in oncology, biological psychiatry, and artificial intelligence he created NetraMark Corp where he has been developing novel technologies that aid in the understanding of our molecular and brain circuitry in addition to novel machine learning algorithms specialized to help understand complex patient populations. He is also a professor of Molecular Medicine at Queen's University in Ontario. Dr. Geraci has a strong interest in advancing the mathematical methods being employed in the study of our molecular circuitry (protein, microRNA, mRNA), the analysis of brain MRIs, and machine learning that can use variables that are beginning to emerge due to our interaction with technologies like fitness watches and smart buildings. A major interest of his is an ongoing project involving translating the vast amount of genetic and proteomic patient data, coupled with our current knowledge of our molecular circuitry, into a scoring scheme that can reveal potential new drug targets.

### Combining Augmented Reality with Deep Learning for Cancer Diagnostics

Right: A picture of the prototype which has been retrofitted into a typical clinical-grade light microscope. Applications of deep learning in medical disciplines including ophthalmology, dermatology, radiology and pathology have recently shown great promise to increase both the accuracy and availability of high-quality healthcare. To further this technology, Google researchers have developed a tool that combines augmented reality with a deep learning neural network to provide pathologists with help in spotting cancerous cells on slides under a microscope. The prototype Augmented Reality Microscope (ARM) platform consists of a modified light microscope that enables real-time image analysis and presentation of the results of machine learning algorithms directly into the field of view. The ARM can be retrofitted into existing light microscopes found in hospitals and clinics by using low-cost, readily available components, and without the need for whole slide digital versions of the tissue being analyzed.

### Profiting from Python & Machine Learning in the Financial Markets

I finally beat the S&P 500 by 10%. This might not sound like much but when we're dealing with large amounts of capital and with good liquidity, the profits are pretty sweet for a hedge fund. More aggressive approaches have resulted in much higher returns. It all started after I read a paper by Gur Huberman titled "Contagious Speculation and a Cure for Cancer: A Non-Event that Made Stock Prices Soar," (with Tomer Regev, Journal of Finance, February 2001, Vol. "A Sunday New York Times article on a potential development of new cancer-curing drugs caused EntreMed's stock price to rise from 12.063 at the Friday close, to open at 85 and close near 52 on Monday.