# Leveraging Prior Knowledge

the not-so-secret technique to juice more nutrients out of data

Canonical: https://astro-blog-bice-chi.vercel.app/blog/prior-knowledge

Edition: en · Language: en · Revision: 1

Archived: 2025-07-09

The archive date records this article’s first appearance in this repository; its original publication date is unknown.

Trust me, I **DO** have conviction in the scaling law—the paradigm that data + compute is all you need.

I find the “less structure, more intelligent”[^1] idea very appealing.

**BUT**, we are just not there yet, at least in the medical imaging domain.

So, here we are, leveraging prior knowledge (aka domain knowledge) to boost training efficiency and data utilization.

Before I dive into medical imaging specifics, I would like to point out:

## [Many natural imaging CV tasks leverage prior knowledge as well](https://astro-blog-bice-chi.vercel.app/blog/prior-knowledge#many-natural-imaging-cv-tasks-leverage-prior-knowledge-as-well)

1.  I would argue that pose landmark detection relies on a **HUMAN-DEFINED** interpretation of skeletal structure where to put nodes and edges is largely application driven , instead of a naturally learned representation.
2.  BEV feature aggregation is predefined by human-calculated epipolar consistency.
3.  Speaking of autonomous vehicles, this target trajectory prediction paper on modeling the coordinate system in transformer positional embedding is pretty interesting[^2].

## [Radiologists utilize prior knowledge to build better visualizations](https://astro-blog-bice-chi.vercel.app/blog/prior-knowledge#radiologists-utilize-prior-knowledge-to-build-better-visualizations)

1.  Curved multiplanar reformation (CPR) and stretched multiplanar reformation (sMPR) are commonly used to diagnose coronary disease. These techniques essentially stretch the 3D vessel along a plane into a straight line for easier interpretation of its structure and interior by eliminating other information from vast 3D voxels.
    
    ![CPR and sMPR](https://astro-blog-bice-chi.vercel.app/assets/blog/image-prior/cpr.webp)
    
    [Top: CPR, Bottom: sMPR](https://www.researchgate.net/figure/Stretched-multiplanar-reformation-sMPR-and-curved-multiplanar-reformation-cMPR-images_fig3_344468596)
    
2.  Similarly, rib-unfolding visualizations make it easier and faster to pinpoint rib fractures, even for non-experts.
    
    ![rib unfolding](https://astro-blog-bice-chi.vercel.app/assets/blog/image-prior/rib.webp)
    
    [Bottom right is the rib-unfolded visualization](https://link.springer.com/article/10.1007/s00330-015-3598-2)
    

## [Examples in medical imaging analysis](https://astro-blog-bice-chi.vercel.app/blog/prior-knowledge#examples-in-medical-imaging-analysis)

> There are **MANY** more—I just name a few I’ve encountered over the years

1.  Mass detection in mammography: mass features should be correlated across multiple views
    
    ![mammo](https://astro-blog-bice-chi.vercel.app/assets/blog/image-prior/mammo.webp)
    
    [Cross-view Correspondence Reasoning based on Bipartite Graph Convolutional Network for Mammogram Mass Detection](https://openaccess.thecvf.com/content_CVPR_2020/papers/Liu_Cross-View_Correspondence_Reasoning_Based_on_Bipartite_Graph_Convolutional_Network_for_CVPR_2020_paper.pdf)
    
2.  Vertebra localization and identification in CT: vertebrae should form a coherent centerline along the spine
    
    ![spine](https://astro-blog-bice-chi.vercel.app/assets/blog/image-prior/spine.webp)
    
    [Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-constrained Optimization](https://arxiv.org/pdf/2012.07947)
    
3.  Suspicious node malignancy prediction: should account for surrounding lymph node conditions
    
    ![PDAC](https://astro-blog-bice-chi.vercel.app/assets/blog/image-prior/PDAC.webp)
    
    [A deep local attention network for pre-operative lymph node metastasis prediction in pancreatic cancer via multiphase CT imaging](https://arxiv.org/pdf/2301.01448)
    
4.  Vessel centerline extraction: easier/better to model with graphs (nodes) than with segmentation (voxels)
    
    ![vessel](https://astro-blog-bice-chi.vercel.app/assets/blog/image-prior/vessel.webp)
    
    [DeformCL: Learning Deformable Centerline Representation for Vessel Extraction in 3D Medical Image](https://arxiv.org/pdf/2506.05820)
    

Not to mention, algorithms also build on top of specialized reconstructions for human interpretation. Unsurprisingly, what’s easier for radiologists to tell is also easier for models to learn.

## [What is leveraging prior knowledge anyway?](https://astro-blog-bice-chi.vercel.app/blog/prior-knowledge#what-is-leveraging-prior-knowledge-anyway)

Essentially, leveraging prior knowledge is the act/technique of carrying biologically/physically/pathologically grounded knowledge into the model, in the form of “tensor affinity”:

1.  Choose a level of abstraction: pixel space, spatial feature space, semantic feature space, instance feature space, etc.
2.  Heuristically set “communication rules” attention map is a better term, but it may mislead reader to think everything in transformer (which tensors should communicate with which)
3.  Choose a method of communication (feature aggregation) - transformer is the default
4.  Note the result might be warped into a whole new space manifold , e.g., dense pixels → sparse nodes, and vice versa

## [Prior knowledge in medical imaging](https://astro-blog-bice-chi.vercel.app/blog/prior-knowledge#prior-knowledge-in-medical-imaging)

The most important prior of the human body we should leverage is the **anatomical prior**:

*   **Landmarks**: Key anatomical reference points
*   **Relative stability**: Position and shape relationships between body parts remain relatively stable
*   **Symmetry**: Bilateral symmetry in many structures
*   **Topological structure**: I find this particularly interesting but under-explored. Most approaches assume oversimplified topology and utilize mathematical tools like Betti numbers to model it

The second most important prior is the **human biological system**:

*   Diseases affecting one biological system often impact multiple parts of that system concurrently

Sources & further reading

## References

[^1]: Don’t teach. Incentivize: [https://docs.google.com/presentation/d/1nnjXIuN2XDJENAOaKXI5srQscO3276svvP6JgivTv6w/edit?usp=sharing](https://docs.google.com/presentation/d/1nnjXIuN2XDJENAOaKXI5srQscO3276svvP6JgivTv6w/edit?usp=sharing)

[^2]: [https://zhuanlan.zhihu.com/p/1892544718539306231](https://zhuanlan.zhihu.com/p/1892544718539306231)