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Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

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Clinically Aware Learning: Ordinal Loss Improves Medical Image Classifiers.

Arsenii Litvinov¹, Egor Ushakov¹, Sofia Senotrusova¹

¹Trusted AI Research Center, RAS, 109004 Moscow, Russia.

Journal of Clinical Medicine

|January 10, 2026

Summary

Incorporating ordinal-aware loss functions significantly improves Breast Imaging Reporting and Data System (BI-RADS) classification accuracy for mammograms. This approach better reflects the clinical severity of misclassifications, enhancing early breast cancer detection reliability.

Keywords:

breast cancer screening breast imaging risk classification deep learning loss functions ordinal classification

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Area of Science:

Medical Imaging AI
Machine Learning for Healthcare
Breast Cancer Screening

Background:

Breast Imaging Reporting and Data System (BI-RADS) is crucial for breast cancer detection.
Current models often treat BI-RADS as nominal, ignoring its ordinal nature and the clinical impact of misclassification.
The mismatch between model optimization and clinical severity is an underexplored issue.

Purpose of the Study:

To evaluate if ordinal-aware loss functions enhance BI-RADS classification performance.
To compare ordinal losses against standard cross-entropy under controlled conditions.
To analyze the impact of dataset and label balancing on performance.

Main Methods:

Systematic evaluation of ordinal-aware loss functions (e.g., Earth Mover Distance) versus cross-entropy.
Unified training pipeline across multiple datasets with fixed architecture.
Analysis of dataset and label balancing strategies.
Performance measured by Area Under the Receiver Operating Characteristic Curve (AUROC) and macro-F1 scores.

Main Results:

Balanced sampling during training significantly improved performance.
Ordinal loss functions consistently outperformed conventional cross-entropy.
Improvements were particularly noted in reducing severe misclassifications, enhancing clinical relevance.
Earth Mover Distance (EMD) demonstrated superior performance.

Conclusions:

Aligning learning objectives with the ordinal BI-RADS structure substantially improves classification accuracy.
Ordinal-aware approaches enhance AI model robustness and clinical relevance in breast cancer screening.
Emphasizes the importance of loss function design, regularization, and data balancing in medical AI.