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

• URL: http://arxiv.org/abs/2504.04025v1
• Date: Sat, 05 Apr 2025 02:33:34 GMT
• Title: Artificial intelligence application in lymphoma diagnosis: from Convolutional Neural Network to Vision Transformer
• Authors: Daniel Rivera, Jacob Huddin, Alexander Banerjee, Rongzhen Zhang, Brenda Mai, Hanadi El Achi, Jacob Armstrong, Amer Wahed, Andy Nguyen,
• Abstract summary: We compare the classification performance of vision transformer to our previously designed convolutional neural network on the same dataset.<n>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.
• Score: 34.04248949660201
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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