MC-GAT: multi-layer collaborative generative adversarial transformer for cholangiocarcinoma classification from...

Area of Science:

• Digital pathology
• Medical imaging analysis
• Artificial intelligence in oncology

Background:

• Accurate histopathological analysis is crucial for early cholangiocarcinoma (CCA) diagnosis.
• Hyperspectral pathological images offer rich spectral information compared to color images.
• Existing Convolutional Neural Network (CNN) and Vision Transformer (ViT) methods face challenges with spectral sequence distortion and diminishing expressive power, respectively, especially with limited labeled hyperspectral imaging (HSI) data.

Purpose of the Study:

• To propose a novel Multi-Layer Collaborative Generative Adversarial Transformer (MC-GAT) for enhanced CCA classification from HSI data.
• To address limitations of existing deep learning models in handling spectral information and limited labeled samples in HSI classification.
• To improve the generalization and discriminating power of models for CCA histopathological analysis.

Main Methods:

• Developed MC-GAT, a generative adversarial network comprising a generator and a discriminator, both based on transformer architectures.
• The generator creates synthetic HSI samples from noise sequences to augment the limited real data.
• The discriminator utilizes a multi-layer collaborative transformer encoder to integrate features from different layers, enhancing its ability to distinguish between real and fake samples.

Main Results:

• MC-GAT demonstrated superior classification performance compared to state-of-the-art methods on the Multidimensional Choledoch Datasets.
• The proposed method effectively integrates spectral and spatial information from HSI data.
• The generative approach improved model generalization by confusing the discriminator with mixed real and fake samples.

Conclusions:

• The MC-GAT method shows significant potential for improving the accuracy of CCA histopathological analysis using hyperspectral imagery.
• This approach offers a promising solution for leveraging HSI data in cancer diagnostics, particularly when labeled samples are scarce.
• MC-GAT can aid pathologists in making more accurate and timely diagnoses of cholangiocarcinoma.