Goto

Collaborating Authors

DeepMind expands AI cancer research program to Japan

#artificialintelligence

DeepMind is furthering its cancer research efforts with a newly announced partnership. Today, the London-based Google subsidiary said it has been given access to mammograms from roughly 30,000 women that were taken at Jikei University Hospital in Tokyo, Japan between 2007 and 2018. It'll use that data to refine its artificially intelligent (AI) breast cancer detection algorithms. Over the course of the next five years, DeepMind researchers will review the 30,000 images, along with 3,500 images from magnetic resonance imaging (MRI) scans and historical mammograms provided by the U.K.'s Optimam (an image database of over 80,000 scans extracted from the NHS' National Breast Screening System), to investigate whether its AI systems can accurately spot signs of cancerous tissue. The collaboration builds on DeepMind's work with the Cancer Research UK Imperial Center at Imperial College London, where it has already analyzed roughly 7,500 mammograms.


Applying machine learning to mammography screening for breast cancer DeepMind

#artificialintelligence

We founded DeepMind Health to develop technologies that could help address some of society's toughest challenges. So we're very excited to announce that our latest research partnership will focus on breast cancer.


AI to fight breast cancer? - L'Atelier BNP Paribas

#artificialintelligence

Constant scientific innovation and new technologies improve cancer detection and treatment worldwide. Imperial College London will lead, for at least 12 months, a consortium of clinicians and academics, which will collaborate with DeepMind Health and the Google AI health research team to develop more accurate breast cancer diagnosis thanks to artificial intelligence. Based at the Cancer Research UK Imperial Centre, the project will explore whether machine learning could make breast screening technologies more efficient than they are nowadays – mammograms, which are X-rays images of the breasts, miss thousands of cases every year, including an estimated 30% of interval cancers. The consortium will use machine learning technology on about 7,500 depersonalised mammography images from the OPTIMAM mammography database to train computer algorithms to better analyse screenings and identify cancerous tissue more accurately. This would lead to earlier detection and treatment. If successful, the technology could benefit millions of women: according to the last available data from the World Cancer Research Fund International, breast cancer is the most common cancer among women in the world, and the second most frequent cancer overall.


Google's DeepMind created an AI for spotting breast cancer that can outperform human radiologists

#artificialintelligence

Google Health and DeepMind have created an AI tool capable of spotting breast cancer with as much accuracy as a human radiologist, according to a new paper published in Nature on Wednesday. To train the model the researchers used two data sets of breast scans from the UK and the US. The UK dataset included scans from 25,856 women, while the US set contained mammograms from 3,097 women. Applying the AI resulted in a reduction of false negatives of 9.4% for the US dataset and 2.7% for the UK. There was a slightly smaller reduction for false positives, 5.7% for the US dataset and 1.2% for the UK one.


New DeepMind AI 'spots breast cancer better than clinicians'

#artificialintelligence

A newly developed artificial intelligence (AI) model is able to spot breast cancer better than a clinician, new research has suggested. Google DeepMind, in partnership with Cancer Research UK Imperial Centre, Northwestern University and Royal Surrey County Hospital, has developed the model which can spot cancer in breast screening mammograms in a bid to improve health outcomes and ease pressure on overstretched radiology services. Initial findings, published by the technology giant in the journal Nature, suggest the AI can identify the disease with greater accuracy, fewer false positives and fewer false negatives. The model, trained on de-identified data of 76,000 women in the UK and more than 15,000 women in the US, reportedly lowered false positive results by 1.2% and false negatives by 2.7% in the UK, but is yet to be tested in clinical studies. When tested, the AI system processed only the latest available mammogram of a patient, whereas clinicians had access to patient histories and prior mammograms to make an informed screening decision.