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AI More Efficient in Detecting Skin Cancer Than Doctors, Finds Study

On average, flesh and blood dermatologists accurately detected 86.6 percent of skin cancers from the images, compared to 95 percent for the CNN.

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A team from Germany, the United States and France taught an artificial intelligence system to distinguish dangerous skin lesions from benign ones, showing it more than 100,000 images.
There are about 232,000 new cases of melanoma, and 55,500 deaths, in the world each year, the research added.

A computer was better than human dermatologists at detecting skin cancer in a study that pitted human against machine in the quest for better, faster diagnostics, researchers said Tuesday.

A team from Germany, the United States and France taught an artificial intelligence system to distinguish dangerous skin lesions from benign ones, showing it more than 100,000 images.

The machine — a deep learning convolutional neural network or CNN — was then tested against 58 dermatologists from 17 countries, shown photos of malignant melanomas and benign moles.

Just over half the dermatologists were at “expert” level with more than five years of experience, 19 percent had between two and five years’ experience, and 29 percent were beginners with less than two years under their belt.

“Most dermatologists were outperformed by the CNN,” the research team wrote in a paper published in the journal Annals of Oncology.

On average, flesh and blood dermatologists accurately detected 86.6 percent of skin cancers from the images, compared to 95 percent for the CNN.

“The CNN missed fewer melanomas, meaning it had a higher sensitivity than the dermatologists,” the study’s first author Holger Haenssle of the University of Heidelberg said in a statement.

A team from Germany, the United States and France taught an artificial intelligence system to distinguish dangerous skin lesions from benign ones, showing it more than 100,000 images.
On average, flesh and blood dermatologists accurately detected 86.6 percent of skin cancers from the images, compared to 95 percent for the CNN. Pixabay

It also “misdiagnosed fewer benign moles as malignant melanoma… this would result in less unnecessary surgery.”

The dermatologists’ performance improved when they were given more information of the patients and their skin lesions.

The team said AI may be a useful tool for faster, easier diagnosis of skin cancer, allowing surgical removal before it spreads.

There are about 232,000 new cases of melanoma, and 55,500 deaths, in the world each year, they added.

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But it is unlikely that a machine will take over from human doctors entirely, rather functioning as an aid.

Melanoma in some parts of the body, such as the fingers, toes and scalp, are difficult to image, and AI may have difficulty recognizing “atypical” lesions or ones that patients themselves are unaware of.

“Currently, there is no substitute for a thorough clinical examination,” experts Victoria Mar from Monash University in Melbourne and Peter Soyer of the University of Queensland wrote in an editorial published with the study. (VOA)

 

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Internship at IIT Bombay: How an Online Training came to my Rescue

Arpit Jindal shares the importance of online training

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C++ internship
Arpit Jindal joined the internshala training for C++ and this helped him with his internship at IIT Bombay. Pixabay

About the Author: Arpit Jindal is pursuing B.Tech in Civil Engineering from IIT Roorkee. He joined Internshala Trainings for Programming with C and C++ training and shares how it helped him complete his internship at IIT Bombay.

Having come from a socially, educationally, and technically backward city, I had no familiarity with coding during my school years. Thus, my initial exposure to coding after getting admission in IIT Roorkee fascinated me to the core. I developed a keen interest in C++ programming in my first semester.

I was seeking an internship at IIT Bombay under Prof. Subimal Ghosh, so I sent him an email expressing my interest to work with him. He reverted asking about my working knowledge of C++, the profile I was interested in, and the time period for which I was available. He shared some of his research papers with me and when I had gained an initial insight on them, he asked me to join him at IIT Bombay. My joy knew no bounds when I was selected for the internship but then began the journey of a training that was to change my internship experience.

Arpit Jindal internship
Arpit Jindal was seeking an internship at IIT Bombay under Prof. Subimal Ghosh.

The professor was working in the field of climatic changes, statistical downscaling of rainfall, the projection of rainfall, etc. using MATLAB software. My job was to convert the MATLAB codes, written to get the projection of rainfall, into C++ codes. Although I was acquainted with the concepts of C++, I needed to enhance my learning and skills for the internship. While digging into available training programs, I came across Internshala’s Young Achiever Scholarship which provides free training to economically challenged students. This was a golden opportunity for me to learn in the comfort of my room. I applied for the scholarship and got the chance to do free training.

The training commenced on June 15. The course was divided into four modules which were designed in an easy and detailed manner and the concepts were explained well. An interesting thing about these was that I could not skip any lesson or test. Normally, with some prior knowledge, we tend to go through only those lessons that we need to learn. However, I had to go through all the lessons and tests which helped strengthen the basics of the subject.This learning not only boosted my knowledge but also my confidence. Another thing that I appreciated about the training program was the support of the team – whenever I had any doubt, I just called them and got it sorted. This made the training program stand out from other courses available on the internet.

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The training program guided me through all the roadblocks I faced during my internship. Everything I needed to work on during the internship became easier (and sometimes possible) with the detailed lessons and practice tests. Conversion of codes required the knowledge of pointers, functions, gamma distribution, inverse gamma distribution, inverse normal distribution, and nested loops which I had no understanding of until this training came to my rescue. I completed my project in time and submitted the project report to my professor. The training helped me like Akshay Kumar had helped Paresh Rawal in the movie ‘Oh My God’, guiding me at each step, and helping me understand the nitty-gritty of C++. Pursuing this training proved to be a great decision and excited me enough to take up another training on AutoCAD in the coming summer.

Courtesy: Internshala Trainings, a training platform (trainings.internshala.com)