🆕 Define DeepFeatureExtractor

[shaneahmed]

🚀 Summary

This PR introduces an updated, DeepFeatureExtractor engine, to the TIAToolbox framework. It enables extraction of deep CNN features from whole slide images (WSIs) or image patches for downstream tasks such as clustering, visualization, or training other models. The PR also includes a command-line interface (CLI) for this engine, along with comprehensive tests.

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✨ Key Features

• New Engine: DeepFeatureExtractor

  • Extracts intermediate CNN features from WSIs and patches.
  • Outputs features and coordinates in Zarr or dict format.
  • Supports memory-aware caching for large-scale processing.

• CLI Integration

  • Adds deep-feature-extractor command to TIAToolbox CLI.
  • Supports input/output paths, model selection, batch size, device, and more.

• Unit Tests

  • Covers patch-based and WSI-based inference.
  • Tests multi-GPU support and CLI functionality.
  • Validates compatibility with CNNBackbone and TimmBackbone models.

• Codebase Integration

  • Registers the engine and CLI in __init__.py files.
  • Updates CLI and model registries to include the new extractor.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 94.92%. Comparing base (c535eab) to head (5c07200).

Additional details and impacted files

@@                    Coverage Diff                     @@
##           dev-define-engines-abc     #963      +/-   ##
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+ Coverage                   94.72%   94.92%   +0.20%     
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  Files                          73       75       +2     
  Lines                        9234     9344     +110     
  Branches                     1208     1214       +6     
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+ Hits                         8747     8870     +123     
+ Misses                        452      439      -13     
  Partials                       35       35              

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[Copilot]

Pull Request Overview

This PR introduces the DeepFeatureExtractor engine to TIAToolbox, enabling extraction of deep CNN feature representations from whole slide images (WSIs) and image patches for downstream tasks like clustering and visualization.

Key Changes

• New DeepFeatureExtractor class extending SemanticSegmentor for feature extraction from WSIs and patches
• CLI integration with deep-feature-extractor command supporting various configuration options
• Comprehensive test suite covering patch-based inference, WSI processing, multi-GPU support, and CLI functionality

Reviewed Changes

Copilot reviewed 8 out of 8 changed files in this pull request and generated 11 comments.

Show a summary per file

File	Description
tiatoolbox/models/engine/deep_feature_extractor.py	Core engine implementation with memory-aware caching and Zarr output support
tiatoolbox/models/engine/init.py	Registration of new deep_feature_extractor module
tiatoolbox/models/init.py	Export of DeepFeatureExtractor class
tiatoolbox/cli/deep_feature_extractor.py	CLI interface for the feature extractor with parameter handling
tiatoolbox/cli/init.py	Registration of deep_feature_extractor CLI command
tiatoolbox/models/engine/semantic_segmentor.py	Fixed docstring to correctly reference SemanticSegmentor instead of PatchPredictor
tests/engines/test_feature_extractor.py	Comprehensive tests for patch/WSI inference, multi-GPU, and CLI
tests/engines/test_semantic_segmentor.py	Updated test docstring for clarity

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[shaneahmed]

Suggested change

	keys = ["probabilities", "coordinates"]
	keys = ["features", "coordinates"]