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Research#Medical Imaging🔬 ResearchAnalyzed: Jan 10, 2026 09:28

MedNeXt-v2: Advancing 3D ConvNets for Medical Image Segmentation

Published:Dec 19, 2025 16:45
1 min read
ArXiv

Analysis

This research introduces MedNeXt-v2, demonstrating advancements in 3D convolutional neural networks for medical image segmentation. The focus on large-scale supervised learning signifies a push towards more robust and generalizable models for healthcare applications.
Reference

“MedNeXt-v2 focuses on scaling 3D ConvNets for large-scale supervised representation learning in medical image segmentation.”

PermalinkArXiv
Research#Computer Vision📝 BlogAnalyzed: Jan 3, 2026 06:57

Computing Receptive Fields of Convolutional Neural Networks

Published:Nov 4, 2019 20:00
1 min read
Distill

Analysis

The article focuses on a technical aspect of convolutional neural networks (CNNs), specifically analyzing their receptive fields. This suggests a focus on understanding and potentially optimizing the internal workings of CNNs. The source, Distill, is known for its high-quality, in-depth explanations, indicating a likely rigorous and detailed treatment of the subject.
Reference

“Detailed derivations and open-source code to analyze the receptive fields of convnets.”

PermalinkDistill
Research#AI Applications📝 BlogAnalyzed: Dec 29, 2025 01:43

What a Deep Neural Network Thinks About Your #Selfie

Published:Oct 25, 2015 11:00
1 min read
Andrej Karpathy

Analysis

This article describes a fun experiment using a Convolutional Neural Network (ConvNet) to classify selfies. The author, Andrej Karpathy, plans to train a 140-million-parameter ConvNet on 2 million selfies to distinguish between good and bad ones. The article highlights the versatility of ConvNets, showcasing their applications in various fields like image recognition, medical imaging, and character recognition. The author's approach is lighthearted, emphasizing the potential for learning how to take better selfies while exploring the capabilities of these powerful models. The article serves as an accessible introduction to ConvNets and their applications.

Key Takeaways

Reference

“We’ll take a powerful, 140-million-parameter state-of-the-art Convolutional Neural Network, feed it 2 million selfies from the internet, and train it to classify good selfies from bad ones.”

PermalinkAndrej Karpathy