A collection of resources on applications of Transformers in Medical Imaging.

Official Implementation of SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation (CVPR 2024)

PyTorch 3D U-Net implementation for Multimodal Brain Tumor Segmentation (BraTS 2021)

[Brainlesion 2021] Official PyTorch Implementation for Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task

Solution of the RSNA/ASNR/MICCAI Brain Tumor Segmentation (BraTS) Challenge 2021

Brain Tumor Image segmentation-Brats2019, 2020, 2021

Brain Tumor Segmentation in Multi-Modal MRI Using Deep Learning

Open source code currently ready for submission, SAMRNet structure for brain tumor segmentation

Quantifying Nyquist-Shannon violations in CNN segmentation encoders and recovering clinical tumor boundary precision (+17.1% BF1) using BlurPool anti-aliasing. Statistically validated on BraTS 2021 with ResNet, VGG, and EfficientNet backbones. Pearson r = -0.865.

MPS-accelerated Brain Tumour Segmentation task based on BraTS '21 task dataset. Uses a hybrid Swin-UNETR + CNN architecture. Optimized for Apple Silicon (M-chip) systems using MPS.

Brain tumor classification based on MGMT methylation status present on the tumor cell.