On the left is a simple drawing of the lungs. The drawing shows the borders of the left and right lung as well as the trachea and the left and right main stem bronchi. The text under the drawing reads: Original image. To the right of the drawing are the 3 additional inputs of RadEdit. They are arranged vertically. On top there is an example editing prompt. It reads "Consolidation". Below there is the same drawing of the lung again but this time the left lung is shaded blue. The text reads: Edit mask according to prompt. Lastly, on the bottom, there is the same drawing of the lung but this time the right lung is shaded red. The text reads: "Do not edit mask". On the right of the 3 additional inputs there is a box saying “RadEdit”. Finally, on the right of the figure, there is the drawing of the lung again. The upper part of the left lung is shaded grey. The text reads: Edited image. Between all the elements, the drawing of the lung, the 3 additional inputs, the box that says “RadEdit”, and the edited image, there are arrows pointing to the next element from left to right.
Microsoft Research Blog

| Max Ilse, Daniel Coelho de Castro, and Javier Alvarez-Valle

RadEdit stress-tests biomedical vision models by simulating dataset shifts through precise image editing. It uses diffusion models to create realistic, synthetic datasets, helping to identify model weaknesses and evaluate robustness.

Outline illustration of Daniela Massiceti next to Martin Grayson
Microsoft Research Podcast

| Amber Tingle, Daniela Massiceti, and Martin Grayson

The personalizable object recognizer Find My Things was recently recognized for accessible design. Researcher Daniela Massiceti and software development engineer Martin Grayson talk about the research project’s origins and the tech advances making it possible.

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Author: Machine Learning Group Time-series forecasting is crucial across various industries, including health, energy, commerce, climate, etc. Accurate forecasts over different prediction horizons are essential for both short-term and long-term planning needs across these domains. For instance, during a public health…

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Author: Youshan Miao Today, deep learning has permeated our daily lives. As the size of models continues to grow, training these models on massive GPU accelerators has become increasingly time-consuming and costly. To effectively harness the power of massive GPUs…

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Dinis Guarda interviews Juan M. Lavista Ferres, Microsoft CVP and Chief Data Scientist, and Director of Microsoft AI for Good Lab, to discuss the key AI For Good initiatives taken by Microsoft for the benefit of humanity. The Dinis Guarda YouTube Podcast is powered by Businessabc.net, Citiesabc.com,…

Research Forum | Episode 4 - abstract chalkboard background with colorful network nodes and circular icons
Microsoft Research Blog

Explore multimodal & small language models, plus advanced benchmarks for AI evaluation. Microsoft researchers are working on breakthroughs in weather prediction, materials design, even a new kind of computer for AI inference and hard optimization problems.

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编者按：在使用 AI 驱动的数据可视化工具时，你是否遇到过这些难题？比如，当你想要设计图表时，需要一次性描述你的可视化需求，既冗杂又繁琐；再比如，当你想要更改图表设计时，又需要从头重新输入一遍你的文本提示，AI 可能还会出错。现在，微软雷德蒙研究院深度学习组推出了 Data Formulator 工具。结合图形化用户界面和自然语言输入，该工具可以更加智能地完成你的数据可视化需求，它不仅支持通过简便...

Research Focus | September 23, 2024
Microsoft Research Blog

Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. Time-series forecasting is a technique used to predict future values based on previously…

In the news | LATAM Microsoft Stories

The Amazon rainforest is the world’s most complex biological environment, with millions of species of plants, birds and wildlife calling the vast area home, many of which have yet to be studied by scientists. From 2022 to 2023, the Amazon…