---

name: learning-deeplearn description: "Use ANTsPy statistical learning and deep-learning helper utilities for decompositions, patches, augmentation, one-hot labels, and bounded array/image preparation without training neural networks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

ANTsPy Learning and Deeplearn Helpers

Use this sub-skill when a task asks for ANTsPy helper utilities around statistical learning, sparse decomposition, eigenanatomy initialization, patch extraction/reconstruction, random augmentation, one-hot segmentation arrays, simulated bias fields, histogram intensity warping, regression intensity matching, or crop/pad sizing for learning pipelines.

Start Here

1. Import the public package as import ants; the package distribution is antspyx.
2. Keep the scope clear: ANTsPy supplies helper functions for image/array preparation and statistical decomposition, not full neural-network model training.
3. Check verified public signatures, return keys, and shape contracts in API reference.
4. Use workflows for patch extraction/reconstruction, paired augmentation, one-hot conversion, intensity simulation, regression matching, and decomposition recipes.
5. Use troubleshooting when dimensions, channel order, paired image lists, randomness, optional dependencies, or patch counts fail.
6. Run scripts/antspy_deeplearn_smoke.py for a tiny bounded in-memory helper check in an environment with antspyx installed.

Core Contracts

• ants.deeplearn helpers are data-preparation utilities: they produce images, arrays, transforms, and labels that can feed external ML code; they do not define, compile, fit, or evaluate neural-network models.
• input_image_list for augmentation is a list of subject lists, where each inner list holds co-registered modalities for one subject: [[subject0_mod0, subject0_mod1], [subject1_mod0, subject1_mod1]].
• Patch utilities support 2-D and 3-D scalar or component images. Array patches are shaped as (n_patches, *patch_size) for scalar images and (n_patches, *patch_size, components) for component images.
• One-hot conversion uses channel-last by default ((*image_shape, n_labels)) and channel-first only when channel_first_ordering=True ((n_labels, *image_shape)).
• Random augmentation and simulation helpers use NumPy and Python randomness internally; set seeds around calls when repeatability matters, and verify returned transform metadata before pairing outputs with labels or points.
• ants.sparse_decom2 expects two matrices with the same row count; it does not scale matrices internally, so center/scale decisions belong to the caller.

Route Elsewhere

• Create, read, write, inspect, clone, compare, and repair ANTsImage metadata: image-core.
• Generic preprocessing, masks, smoothing, denoising, thresholding, morphology, core cropping/padding, resampling, and histogram matching: image-ops-math.
• Registration mechanics, transform files, transform application, displacement fields, and point transform semantics outside augmentation helpers: registration-transforms.
• Segmentation algorithms, label statistics, overlap, centroids, label geometry, and label matrices: segmentation-labels.
• Full model training, ANTsPyNet architectures, loss functions, pretrained networks, and deep learning inference workflows: use ANTsPyNet or another ML framework outside this ANTsPy repo skill.

Boundary Notes

• The module name ants.deeplearn is historical and helper-oriented. Do not promise model-training support from ANTsPy itself.
• crop_image_from_center_point exists in source helper code but was not exported as a public ants function in the verified antspyx 0.6.3 inspection; prefer exported crop/pad helpers unless runtime introspection proves availability.
• data_augmentation and randomly_transform_image_data internally use transform and preprocessing functions. Use this sub-skill for their learning-helper contract; use registration or image-ops sub-skills to debug low-level transform/preprocessing behavior.

References

• API reference: verified signatures, return keys, shape/axis contracts, dependencies, and public-export caveats.
• Workflows: practical helper recipes for patches, augmentation, one-hot arrays, intensity perturbation, regression matching, crop/pad sizing, and decomposition.
• Troubleshooting: shape/axis mismatch, paired transform tracking, randomness, memory/patch count, optional dependency, and helper-vs-training boundaries.