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Hierarchical Deep Learning for Abnormality Classification in Mouse Skeleton Using Multiview X-Ray Images:

Muhammad M Jawaid¹, Rasneer S Bains², Sara Wells²

¹School of Engineering & Physical Sciences, College of Health & Science, University of Lincoln, Brayford Pool, Lincoln LN6 7TS, UK.

Journal of Imaging

|October 28, 2025

Summary

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Classification of Bones

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...

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This summary is machine-generated.

Multi-view imaging significantly enhances skeletal abnormality detection in mice, especially for complex multi-label cases. Hierarchical learning with multiple views improves classification accuracy over single-view methods.

Area of Science:

Biomedical imaging
Machine learning
Computational biology

Background:

Single-view anomaly detection lacks context for multi-label problems.
Multi-view imaging offers richer contextual information for classification tasks.

Purpose of the Study:

To evaluate the efficacy of multi-view (MV) image data with hierarchical learning for skeletal abnormality detection.
To compare the performance of MV classification against single-view methods in a multi-label context.

Main Methods:

Curated a specimen-wise MV dataset from 170,958 International Mouse Phenotyping Consortium (IMPC) images.
Developed two hierarchical classification frameworks using ConvNeXT and convolutional autoencoder (CAE) backbones.
Trained models at three hierarchical levels of increasing anatomical granularity.

Keywords:

convolutional autoencoder hierarchical learning mouse phenotyping multiview representation skeletal abnormality

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Main Results:

MV classification performed comparably to single views at the top hierarchy level (L1, mean AUC 0.95).
Hierarchical models using MV data significantly improved classification at lower levels (L2 and L3) compared to single views (e.g., L2: DV 0.65, LV 0.76, MV 0.87; L3: DV 0.54, LV 0.59, MV 0.82).
Both ConvNeXT and CAE architectures demonstrated the advantage of MV data for detecting specific skeletal abnormalities.

Conclusions:

Multi-view image data combined with hierarchical learning is advantageous for skeletal abnormality detection.
This approach effectively addresses challenges in multi-label anomaly detection by providing enhanced contextual information.