48 citations · 48 across the 3 of their papers we have counts for
3 papers
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★ 48 cited
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…