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Found 6 papers, 1 papers with code
Swin MAE: Masked Autoencoders for Small Datasets
The development of deep learning models in medical image analysis is majorly limited by the lack of large-sized and well-annotated datasets.
An Anatomy-aware Framework for Automatic Segmentation of Parotid Tumor from Multimodal MRI
Second, considering that the segmentation model is prone to be disturbed by similar anatomical structures and make wrong predictions, we design anatomy-aware loss.
Segmentation of Parotid Gland Tumors Using Multimodal MRI and Contrastive Learning
Segmentation of the parotid glands and tumors by MR images is important for the treatment of parotid gland tumors.
Parotid Gland MRI Segmentation Based on Swin-Unet and Multimodal Images
Preoperative tumor localization, differential diagnosis, and subsequent selection of appropriate treatment for parotid gland tumors are critical.
Mutual Attention-based Hybrid Dimensional Network for Multimodal Imaging Computer-aided Diagnosis
Recent works on Multimodal 3D Computer-aided diagnosis have demonstrated that obtaining a competitive automatic diagnosis model when a 3D convolution neural network (CNN) brings more parameters and medical images are scarce remains nontrivial and challenging.
TransMed: Transformers Advance Multi-modal Medical Image Classification
To our best knowledge, this is the first work to apply transformers to medical image classification.
