👉 Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | 💡 The newsletter is read by people from: Nvidia, Baker Hughes, Harvard, NYU, Columbia University, University of Toronto and more!
From DeepSeek to Lung Tumors
DeepSeek: A New Player in AI for HealthcareThe new open-source LLM, DeepSeek, is creating buzz for its potential to transform AI in medicine and healthcare. Designed for transparency and collaboration, it opens doors for innovation in medical research, decision support, and patient care. It's exciting to see what DeepSeek will bring to these conversations—I’ll keep you posted!
Deep learning model halves lung tumor segmentation times
Researchers at Stanford University have developed a cutting-edge deep learning model that halves the time needed to segment lung tumors on CT scans. Trained on one of the largest datasets of its kind—1,504 scans with 1,828 segmented tumors—the model uses an advanced 3D U-Net architecture to deliver near-expert-level performance. Why It Matters This model achieves 92% sensitivity and 82% specificity, with a Dice similarity coefficient (DSC) nearly matching that of radiologists. By reducing segmentation time from an average of 166 seconds to just 76 seconds, the tool enhances efficiency and supports faster treatment planning for lung cancer patients. What’s Next? With its ability to analyze scans from diverse CT equipment and detect smaller lesions, this innovation is poised to revolutionize lung cancer diagnostics and management. TotalSegmentator v2.5Total Segmentator folks have done it again! A new update to this amazing tool has been released. Here's what has been added: MR:
CT:
You can explore TotalSegmentator directly on their github repo here.
That's it for this week's edition, I hope you enjoyed it! |
Machine Learning for Medical Imaging
👉 Learn how to build AI systems for medical imaging domain by leveraging tools and techniques that I share with you! | 💡 The newsletter is read by people from: Nvidia, Baker Hughes, Harvard, NYU, Columbia University, University of Toronto and more!
Hello Reader, Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below ↓ What We Learned This Year (Medical Imaging Edition) As this year wraps up, I wanted to share a few quick lessons from the projects we worked on, especially around building web...
Hello Reader, Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below ↓ Zoom That Works Everywhere If you can’t zoom any pane in your web DICOM viewer, you’re doing extra work for no reason. Think of it like this: when something is small, you bring it...
Hello Reader, Welcome to another edition of PYCAD newsletter where we cover interesting topics in Machine Learning and Computer Vision applied to Medical Imaging. The goal of this newsletter is to help you stay up-to-date and learn important concepts in this amazing field! I've got some cool insights for you below ↓ A Quick Look at Our Volume Measurement Tool One of the tools we’ve been working on is a simple way to estimate 3D volumes directly inside the viewer. You start by drawing a...