Breast cancer is the most-common invasive cancer among women. It affects 14% of women worldwide and every year, over half a million women die of it. That makes breast cancer one of the largest medical problems faced today.
Taking treatment decisions and curing this disease requires, at first place, diagnosing it.
However, due to time constraints and the existence of very small metastases on individual slides, human pathologists sometimes fail to detect cancerous cells: they miss about 20% of breast cancers in mammograms, even in countries where screening mammograms are reviewed by two radiologists.
In thelast years, the introduction of deep learning and convolutional neural networks (CNNs) in medical image analysis has prevaded the field of automated breast cancer detection in digital mammography.
Researchers have created an AI diagnostic tool that helps doctors detect breast cancer.
The man-made neural network is trained with more than a million mammography images, and the program is getting “smarter” as it reviews more and more data.
The AI tool was taught to analyze even the small patches, spot changes that are invisible to the human, and then make a map of the areas that are most at risk.
In addition to its success in the detection of abnormality as well as an average radiologist, this AI invention has won two main challenges concerning the efficiency and consistence of the results: Reducing false negatives by 9% and false positives by 5.7%.
At this level, you might be asking yourself whether or not this program will replace doctors when it comes to detecting breast cancer. The answer is definitely a NO!
In fact, the knowledge we’re getting when the program points out cancer to us, is the knowledge of many pathologists who contributed to training the machine on a model. It’s mainly human knowledge.
All we said above, highlights the fact that we must combine the strengths of human radiologists with the results of these performing systems in order to provide the most accurate diagnosis for the patient and thus allow clinicians to have significantly more time to deal with curing him.
“AI detected pixel-level changes in tissue invisible to the human eye, while humans used forms of reasoning not available to AI,” says senior study author Krzysztof J. Geras, PhD, assistant professor in the Department of Radiology at NYU Langone and an affiliated faculty member at the NYU Center for Data Science. “The ultimate goal of our work is to augment, not replace, human radiologists.”
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Perseverance Rover: Humanity’s newest feat
February 19th, 2021 marks a new historical event in the space exploration journey, as NASA scientists rejoice.
NASA’s 2020 Mars mission successfully landed on Mars. This mission includes the Perseverance Rover along with other experiments.
While descending to touch down on the red planet, we got a close-up picture of the Perseverance rover, the first-ever high-resolution color image to be sent back by the Hazard Cameras that is most likely to become a classic photograph in the history of spaceflight.
A 360° rotation of its mast allowed the Mastcam-Z instrument to capture its first panorama, and we also got the first-ever audio recording from the red planet thanks to a microphone on the rover.
The camera system covered the whole landing process, showing the intense ride and the so-called « seven minutes of terror » descent sequence.
“Touchdown confirmed! Perseverance is safely on the surface of Mars, ready to begin seeking the signs of past life.” Said NASA engineer, Swati Mohan, during the live stream of the landing.
This landing is a considerable achievement of engineering that took multiple years to attain and was one of the main difficulties of the mission. As a matter of fact, because Mars’s air is so thin, it was more difficult to slow the spacecraft while descending, and it required a parachute and rockets, among other equipment.
Another challenge that the rover faced was the dangerous terrain it was headed to. Jezero Crater, the new home to the Perseverance Rover, was riskier than previous missions, as it presents deep pits, high cliffs, and big rocks making it harder for the rover to touch down.
However, NASA managed to design new pieces that allowed Perseverance to scan the surface and navigate around any obstacles which was a huge success.
The main goal of this mission is to hunt for the remnants of life and to study the Martian rocks up to 3.8 billion years old.
With this new source of data, the knowledge about our neighboring planet will pave the way to the future, when humans will set foot on the Martian soil.