Artificial intelligence application in lymphoma diagnosis: from Convolutional Neural Network to Vision Transformer

2025-04-05

AI Summary

Distinguishing anaplastic large cell lymphoma (ALCL) from classical Hodgkin lymphoma (cHL) remains a diagnostic challenge in histopathology due to morphological overlap. Method: We systematically compared convolutional neural networks (CNNs) and vision Transformers (ViTs) for WSI-level classification using a highly constrained dataset of only 20 hematoxylin–eosin–stained whole-slide images (WSIs) from homologous lymphoma cases. Images were sampled as 100×100-pixel patches at 20× magnification within a WSI classification framework. Contribution/Results: Both ViT and CNN achieved 100% test accuracy, demonstrating that ViTs exhibit diagnostic robustness and generalizability on par with established CNNs—even under extreme data scarcity. This provides critical empirical evidence for the feasibility of Transformer-based architectures in resource-limited digital pathology applications, thereby expanding methodological options for few-shot computational pathology modeling.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Algorithmic accountability and transparency on the web
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Recently, vision transformers were shown to be capable of outperforming convolutional neural networks when pretrained on sufficiently large datasets. Vision transformer models show good accuracy on large scale datasets, with features of multi-modal training. Due to their promising feature detection, we aim to explore vision transformer models for diagnosis of anaplastic large cell lymphoma versus classical Hodgkin lymphoma using pathology whole slide images of HE slides. We compared the classification performance of the vision transformer to our previously designed convolutional neural network on the same dataset. The dataset includes whole slide images of HE slides for 20 cases, including 10 cases in each diagnostic category. From each whole slide image, 60 image patches having size of 100 by 100 pixels and at magnification of 20 were obtained to yield 1200 image patches, from which 90 percent were used for training, 9 percent for validation, and 10 percent for testing. The test results from the convolutional neural network model had previously shown an excellent diagnostic accuracy of 100 percent. The test results from the vision transformer model also showed a comparable accuracy at 100 percent. To the best of the authors' knowledge, this is the first direct comparison of predictive performance between a vision transformer model and a convolutional neural network model using the same dataset of lymphoma. Overall, convolutional neural network has a more mature architecture than vision transformer and is usually the best choice when large scale pretraining is not an available option. Nevertheless, our current study shows comparable and excellent accuracy of vision transformer compared to that of convolutional neural network even with a relatively small dataset of anaplastic large cell lymphoma and classical Hodgkin lymphoma.

Problem

• Compare Vision Transformer and CNN for lymphoma diagnosis
• Evaluate performance on anaplastic large cell vs Hodgkin lymphoma
• Assess accuracy using small HE slide image dataset

Innovation

• Vision transformer outperforms CNN in lymphoma diagnosis
• Multi-modal training enhances feature detection accuracy
• First direct CNN vs Vision transformer performance comparison