Add paper summary: nnU-Net (arXiv:1809.10486)

[claude]

Objective

Automatically summarize arXiv paper from Issue #642.

Effect

• Paper: nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
• Summary file: machine-learning/2018/nnu-net-self-adapting-framework-for-u-net-based-medical-image-segmentation.md
• Issue: Closes nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation #642

This PR includes a comprehensive summary following the project's DoD requirements:

• Concrete, detailed explanations of the self-configuring pipeline (preprocessing, training, inference)
• Clear input/output specifications with tensor dimensions for all three U-Net variants
• Mathematical formulations for Dice loss and combined loss
• All seven Medical Segmentation Decathlon datasets explicitly listed
• Comparison table with original U-Net, Attention U-Net, Dense U-Net, and V-Net

Test

• Review the summary for completeness and accuracy
• Verify all mathematical formulations have proper dimensions
• Check that DoD requirements are met (see checklist below)
• Confirm the paper URL matches the issue

Note

Automatically generated via the auto-summarize-papers workflow.

Closes #642

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Definition of Done Checklist

Common

• Describe the concrete sentences to support understanding (not just writing "I understand ...")
• Describe the condition which can be applied (who, when, where)
• Include information about licenses and copyrights

Computer Science / Machine Learning

• Clear Input and Output
• Describe Algorithms with pseudocode
• Explain datasets used
• Clear calculation order
• Describe the difference between similar algorithms