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Updated: May 28, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Automatic Grade Classification in Prostate Histopathological Images Using EfficientNet and Ordinal Focal Loss.

Woshington Valdeci de Sousa Rodrigues¹, Armando Luz², José Denes Lima Araújo²

¹Instituto Federal do Piauí (IFPI), Campus Picos, Picos 64605-500, PI, Brazil.

Bioengineering (Basel, Switzerland)

|May 27, 2026

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This study introduces a deep learning method to improve prostate cancer grading from histopathology images. By reducing label noise and using ordinal regression, the system achieved higher accuracy in classifying International Society of Urological Pathology grade groups.

Area of Science:

Digital pathology
Machine learning in oncology
Computational imaging

Background:

Accurate prostate cancer grading is crucial for treatment decisions.
Existing automated methods struggle with high class similarity and label noise in ISUP grading.

Purpose of the Study:

To develop an improved deep learning pipeline for automated International Society of Urological Pathology (ISUP) grade group classification.
To address challenges in prostate histopathology image analysis, including class imbalance and label noise.

Main Methods:

A pipeline using EfficientNet convolutional neural networks with a hybrid loss function (ordinal regression and Focal Loss).
Implementation of a noise-filtering strategy based on prediction entropy to remove uncertain samples.
Reformulation of the classification task as an ordinal regression problem to model grade hierarchy.

Keywords:

EfficientNet PANDA dataset digital pathology focal loss histopathological image classification ordinal regression prostate cancer

Related Experiment Videos

Last Updated: May 28, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Main Results:

Noise filtering improved performance from Cohen's kappa (κ) of 0.826 to 0.833.
Incorporating ordinal loss further boosted performance to κ=0.851.
The best model, combining ordinal regression and Focal Loss, achieved κ=0.857 and 0.669 accuracy, reducing severe misclassifications.

Conclusions:

Explicitly modeling the ordinal structure of ISUP grades enhances classification accuracy.
Mitigating label noise through prediction entropy is an effective strategy for improving automated prostate cancer grading.
The proposed deep learning approach offers a promising tool for more accurate prostate cancer grading systems.