How clean are the data?Those are some interesting ideas to think about, also they have developed a level of data cleanliness ranging from level D to A.
Level D → Raw data Level C → de-identification of the data, ethical approval is needed to access the data.
Finally, it is unstructured.
Level B → Structured Data and quality control, no labelsLevel A → Cleaned and Labeled dataFrom all of those steps, getting the ethical approval of the data takes the most amount of time.
Bias and Implications for Medical AI — Matthew P.
Lungren, MD, MPHStarted the talk with an urban legend of using a neural network to classify tanks, like that the model we use might have a bias.
When computer systems have an unexpected outcome, we need to know where those errors were made and why they were made.
In the medical field, there can be three types of bias hospital, Computational, and Cognitive.
Hospital bias → different hospitals use different machines to capture data in different manners.
A model that has been trained from one hospital data source might not do well from another data source.
Computational bias → Adversary attacks.
Cognitive bias → this one is more of human bias since humans tend to behave differently in different situations.
AI in Breast Imaging — Hugh Harvey, MBBS BScPhoto by Jon Tyson on UnsplashInterestingly a lot of data related to breast cancer are originating from Europe.
Some problems in this line of research include different companies using different machines and pre-processing methods to create the mammography images, how can we standardize them?This different sources can create bias also patch based analysis was not successful for breast cancer mammography images.
Even for segmentation, data labeling is key.
Since segmentation is the backbone of a lot of different processes if we don’t get this right might cause some problems.
AI in Body Imaging — Bhavik N.
Patel, MD, MBACT images are extensively used in medical settings, and there is a lot of potentials when paired with AI, starting from segmentation and classification.
However, there are also a lot of challenges, in diagnosis such as lung or prostate cancer classification, noise to signal ratio is very critical.
And overcoming the serious imbalance data distribution is another challenge.
(paired with a small number of training data it gets harder).
Even though the model might have been trained on natural images, using pre-trained models are a very good idea.
Final WordsIt was an amazing conference, and I got to meet Dr.
Ng!.That was a life-changing moment for me.
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I hope some of the information here helped you as well.