AI RESEARCH
Artificial intelligence application in lymphoma diagnosis with Vision Transformer using weakly supervised training
arXiv CS.LG
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ArXi:2604.13795v1 Announce Type: cross Vision transformers (ViT) have been shown to allow for flexible feature detection and can outperform convolutional neural network (CNN) when pre-trained on sufficient data. Due to their promising feature detection capabilities, we deployed ViTs for morphological classification of anaplastic large cell lymphoma (ALCL) versus classic Hodgkin lymphoma (cHL). We had previously designed a ViT model which was trained on a small dataset of 1,200 image patches in fully supervised.