Lakera now offers you the opportunity to easily test whether your algorithms are robust to histological artifacts and variations. It lets you stress test your computer vision models to gain confidence in their robustness properties prior to clinical validation and deployment.
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
In-context learning
As users increasingly rely on Large Language Models (LLMs) to accomplish their daily tasks, their concerns about the potential leakage of private data by these models have surged.
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A: At the beginning there was 10 cookies, then 2 of them were eaten, so 8 cookies were left. Then 5 cookieswere given toa friend, so 3 cookies were left. 3 cookies + 2 boxes of 2 cookies (4 cookies) = 7 cookies. Youhave 7 cookies.
English to French Translation:
Q: A bartender had 20 pints. One customer has broken one pint, another has broken 5 pints. A bartender boughtthree boxes, 4 pints in each. How many pints does bartender have now?
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Lorem ipsum dolor sit amet, Q: I had 10 cookies. I ate 2 of them, and then I gave 5 of them to my friend. My grandma gave me another 2boxes of cookies, with 2 cookies inside each box. How many cookies do I have now?
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A: At the beginning there was 10 cookies, then 2 of them were eaten, so 8 cookies were left. Then 5 cookieswere given toa friend, so 3 cookies were left. 3 cookies + 2 boxes of 2 cookies (4 cookies) = 7 cookies. Youhave 7 cookies.
English to French Translation:
Q: A bartender had 20 pints. One customer has broken one pint, another has broken 5 pints. A bartender boughtthree boxes, 4 pints in each. How many pints does bartender have now?
We are thrilled to announce the release of our robustness testing suite for state-of-the-art computer vision in digital pathology. With the explosion of this new field, the medical sector is experiencing a tremendous flurry of activity, particularly in the space of medical imaging.
**Pro tip:💡 Want to join other pathology companies in automating model validation with MLTest? You can get started in minutes.**
A major headache for pathology imaging, however, is ensuring model robustness during operation. It's not only that histological slides are very heterogeneous, but the abundance of histological artifacts that commonly appear in operation can lead to severe underperformance as well. These include dust particles, oily spots, loose cells from tissue tearing, staining - the list goes on and on. Unfortunately, each one of these culprits can play havoc with your algorithms, leading to missed objects or too much debris masquerading as your target.
Digital pathology teams are already using Lakera to speed up model validation, train better models, and expedite clinical validation and certification processes.
With this latest release, they bring their machine learning testing capabilities to a new level. This test suite includes new types of robustness tests, such as:
Lakera now offers you the opportunity to easily test whether your algorithms are robust to histological artifacts and variations. It lets you stress test your computer vision models to gain confidence in their robustness properties prior to clinical validation and deployment.
Does your tumor detector still work with dust on the slides? Can something as simple as extra tissue cells dramatically increase your number of false positives? If questions like these are on your mind, then head on over to MLTest to learn why leading medical companies trust Lakera.
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
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