JunMa11/SegLossOdyssey

Health v2 · maintenance only

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4007 stars · 610 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A curated collection of loss functions for medical image segmentation, accompanying the 'Loss Odyssey in Medical Image Segmentation' survey paper. It catalogs and links implementations of segmentation losses, highlighting compound losses for imbalanced tasks.

Use cases

• find loss functions for medical image segmentation
• compare segmentation loss functions for imbalanced datasets
• choose a loss for training a U-Net on medical scans
• implement Dice or focal loss variants in PyTorch
• survey recent research on segmentation losses
• improve segmentation performance on small-structure classes

When to choose

• you are training deep learning models for medical image segmentation
• you need to benchmark or select among many segmentation losses
• your segmentation dataset is highly class-imbalanced

When to avoid

• you need a maintained training framework rather than a loss catalog
• your task is general object detection or classification
• you need non-PyTorch implementations out of the box

Facets

library · maturity maintenance

machine-learning deep-learning deep-learning computer-vision healthcare image-processing python loss-functions medical-imaging segmentation pytorch research-collection

Member repositories

For agents

markdown · JSON · MCP: product_card(name="JunMa11/SegLossOdyssey")