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.

So you have been doing some deep learning, training some models using TensorFlow, PyTorch, or whatever library you are fond of. You feel like you are getting a grip on this thing and think it can turn out as a possible career option. Then comes the first professional assignment, it could be a freelance project you take up or something your company assigns to you and boom you feel like a person on a raft out in the sea, and nothing to guide you. Well, when working with datasets of the self-created origin or something that isn't a part of the precreated dataset pipelines created out there then there are several problems you may face. How do I clean this dataset?

An artificial intelligence system that was originally designed to distinguish between different types of pastry in Japan in 2013 was adapted to identify cancers, The New Yorker reports. The system, called "BakeryScan," was first launched back in 2013 by Hisashi Kambe, a computer systems engineer. The system, which is still around and can be bought for roughly $20,000, gives local bakeries a hand by doing basic tasks like distinguishing croissants from bear claws. It can cut down on employee training and make the checkout process more hygienic, according to the company. But several years later, a doctor from the Louis Pasteur Center for Medical Research in Kyoto realized that the clever tool could be used for a very different purpose as well: recognizing cancerous cells in microscope slides.

Artificial intelligence designed to recognize different type of pastries could be a vital tool in the medical world. BakeryScan, developed by Japan-based Brain Co., scans baked good on a tray with a camera and uploads the official name of each to a system for easy checkout at a bakery – but scientists found it can also identify cancer. A doctor from the Louise Pasteur Center for Medical Research in Kyoto had the system revised to spot cancerous cells on a microscope slide with 99 percent accuracy. Instead of investigating doughnut holes and bread ridges, the redesigned system, called Cyto-AisCAN, analyzes a urinary cell to identify and measure its nucleus to determine if it is diseased. BakeryScan, developed by Japan-based Brain Co., scans baked good on a tray with a camera and uploads the official name of each to a system for easy checkout at a bakery – but scientists found it can also identify cancer.

THE ARTICLE WAS ORIGINALLY PUBLISHED ON THE NEXT WEB. AI, Machine Learning, and Big Data open a vast horizon and many opportunities for the Healthcare Industry. When such tech giants as Google or IBM appear in the field of healthcare, we know it is something worth exploring. AI Researcher Frost & Sullivan said artificial intelligence systems will generate $6.7 billion in global revenue from healthcare by 2021, compared with $811 million in 2015. It is bigger than what we think.

According to the American Cancer Society, cancer kills more than 8 million people each year. Early detection can boost survival rates. Researchers and clinicians are feverishly exploring avenues to provide early and accurate diagnoses, as well as more targeted treatments. Blood screenings are used to detect many types of cancers, including liver, ovarian, colon and lung cancers. Current blood screening methods typically rely on affixing biochemical labels to cells or biomolecules.

The fight against Cancer has been seeing a lot of developments with the increasing improvements in technology. However, it seems like the people from Yokohama, Japan have perfected a new development in Cancer Research. This Artificial Intelligence could help detect colorectal cancer even before benign tumors become malignant. The way this works is the AI observes a colorectal polyp magnified by 500 times in order to spot its variations. Afterwards, the program cross-references these variations against a database of over 30,000 images of pre-cancerous and cancerous cells that were used to train the machine-learning program.

An artificial intelligence program that scans thousands of human body cells was able to predict – with 86 percent accuracy – which would become cancerous. The Yokohama, Japan AI study was able to detect colorectal cancer before benign tumors were able to become malignant. The program took microscopic images of a colorectal polyp, magnified it by 500 times and then cross-referenced the variations with more than 30,000 images. The database of images contained both pre-cancerous and cancerous cells, and was the first cross-referencing image research of its kind, Inverse first reported. The AI-assisted system pulled off an impressive 86 percent prediction accuracy rate that was derived after assessing patients with a colorectal polyp diagnosis.

The quest to better detect cancer has made a potentially huge strides. A study out of Yokohama, Japan, has potentially harnessed artificial intelligence to help detect colorectal cancer even before benign tumors become malignant. Here's how the researchers used artificial intelligence to spot cancer: The AI program observed a colorectal polyp magnified by 500 times in order to spot its variations. It then cross-referenced those variations against a database of over 30,000 images of pre-cancerous and cancerous cells that were used to train the machine-learning program. With that knowledge base, the AI was able to make a prediction in under a second.

A project led by Pennsylvania's Lehigh University might help cancer patients avoid undergoing multiple surgeries. The results can be crucial since according to recent studies, 25 percent of women who undergo breast-saving lumpectomy surgery will require a second operation, which incurs a median cost of $16,000 and causes further health complications. After removing the tumor from the breast, doctors have to check the operated area for residual cancerous cells. Currently, the process of examination involves taking tissue samples from the margin of the operated area, freezing them with liquid nitrogen, sectioning them to thin slices, and sending them to a lab for examination. The process, known as histopathology, can take as long as a week.