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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...

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Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

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A systematic comparison of deep learning methods for Gleason grading and scoring.

Juan P Dominguez-Morales¹, Lourdes Duran-Lopez¹, Niccolò Marini²

¹Robotics and Technology of Computers Lab., ETSII-EPS, Universidad de Sevilla, Sevilla 41012, Spain; SCORE Lab, I3US. Universidad de Sevilla, Spain.

Medical Image Analysis

|May 10, 2024

Summary

This study compares deep learning methods for prostate cancer Gleason grading and scoring. Fully supervised learning excels at grading, while CLAM is best for scoring, aiding researchers in selecting optimal approaches.

Keywords:

Computational pathology Deep learning Full supervision Multiple-instance learning Prostate cancer Semi-supervision Weak supervision

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Area of Science:

Computational pathology
Digital histopathology image analysis
Artificial intelligence in oncology

Background:

Prostate cancer diagnosis relies on Gleason score, assessing cellular abnormality in tissue samples.
Computational pathology offers advanced algorithms for analyzing digitized histopathology images.
Lack of consensus exists on optimal deep learning methods for Gleason grading and scoring tasks.

Purpose of the Study:

To systematically compare state-of-the-art deep neural network training approaches for Gleason grading and scoring.
To evaluate performance across nine diverse datasets from pathology institutes and repositories.
To provide guidance on selecting the best methods based on task and data characteristics.

Main Methods:

Systematic comparison of nine datasets using various deep neural network training approaches.
Evaluation of fully-supervised learning, weakly-supervised learning, semi-supervised learning, Additive-MIL, Attention-Based MIL, Dual-Stream MIL, TransMIL, and CLAM.
Application of these methods to Gleason grading and scoring tasks on digitized histopathology images.

Main Results:

Fully supervised learning demonstrated superior performance for Gleason grading tasks.
CLAM (Contextualized Learning Attention Model) proved most effective for Gleason scoring tasks.
Performance varied across methods depending on dataset characteristics and label availability.

Conclusions:

The study identifies optimal deep learning strategies for prostate cancer Gleason assessment.
Fully supervised learning is recommended for Gleason grading, while CLAM is preferred for Gleason scoring.
Findings guide researchers in choosing appropriate computational pathology methods for specific diagnostic challenges.