Deep Gaussian processes for multiple instance learning: Application to CT intracranial hemorrhage detection.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Machine Learning

Background:

• Intracranial hemorrhage (ICH) is a critical medical emergency with high mortality and morbidity rates.
• Early and accurate ICH detection is vital for effective patient treatment.
• Current deep learning models for ICH detection often require time-consuming slice-level radiologist annotations.

Purpose of the Study:

• To develop a deep learning model for accurate intracranial hemorrhage detection using only scan-level annotations.
• To address the limitations of costly and time-consuming slice-level annotations in current diagnostic models.
• To improve the efficiency and accuracy of ICH diagnosis in head CT scans.

Main Methods:

• Formulated intracranial hemorrhage detection as a Multiple Instance Learning (MIL) problem.
• Developed a novel probabilistic method using Deep Gaussian Processes (DGP) for MIL training.
• Employed a Convolutional Neural Network (CNN) with an attention mechanism to extract image features, feeding them into the DGPMIL model.

Main Results:

• The proposed DGPMIL model demonstrated superior performance compared to existing MIL methods and attention-based CNNs.
• Experiments on MNIST showed that multiple Gaussian Process layers enhance performance in complex feature distributions.
• DGPMIL achieved high performance on public datasets (RSNA and CQ500), with AUC-ROC of 0.957 and 0.909, respectively.

Conclusions:

• The DGPMIL model provides accurate slice- and scan-level ICH diagnosis without requiring slice-level annotations.
• This approach significantly reduces the annotation burden on radiologists.
• The DGPMIL model's applicability extends to broader medical image classification tasks.