TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
[Paper Review] DeiT: Training data-efficient image transformers & distillation through attention(Hugo Touvron, 2020)
[Paper Review] Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
[Paper_Review] Xception : Deep Learning with Depthwise Separable Convolutions (Francois Chollet, 2016)
[Paper Review] DINO - Emerging Properties in Self-Supervised Vision Transformers (Mathilde Caron, 2021)
[Paper Review] Self-Supervised_Pre-Training of Swin Transformers for 3D Medical Image Analysis (Yucheng Tang, 2022)
[Paper Review] ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction (Xiaomin Fang, 2022)