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cs.CV2026
Artificial intelligence application in lymphoma diagnosis with Vision Transformer using weakly supervised training
Nghia, Nguyen, Amer Wahed +9
Vision transformers (ViT) have been shown to allow for more flexible feature detection and can outperform convolutional neural network (CNN) when pre-trained on sufficient data. Du…
cs.CV2025
Artificial intelligence application in lymphoma diagnosis: from Convolutional Neural Network to Vision Transformer
Daniel Rivera, Jacob Huddin, Alexander Banerjee +6
Recently, vision transformers were shown to be capable of outperforming convolutional neural networks when pretrained on sufficiently large datasets. Vision transformer models show…
cs.CV2018
Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning
Hanadi El Achi, Tatiana Belousova, Lei Chen +6
Recent studies have shown promising results in using Deep Learning to detect malignancy in whole slide imaging. However, they were limited to just predicting positive or negative f…