☆ 3.8 Proceedings Paper

UNeXt: MLP-Based Rapid Medical Image Segmentation Network

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT V (2022)

Related references

Note: Only part of the references are listed.

Article Computer Science, Artificial Intelligence

ResMLP: Feedforward Networks for Image Classification With Data-Efficient Training

Hugo Touvron et al.

Summary: ResMLP is an image classification architecture that relies solely on multi-layer perceptrons. It achieves accurate and efficient results through a combination of linear layers and a two-layer feed-forward network, trained with modern strategies and data augmentation.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2023)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

S2-MLP: Spatial-Shift MLP Architecture for Vision

Tan Yu et al.

Summary: In this paper, a novel pure MLP architecture called S-2-MLP is proposed, which achieves higher accuracy than MLP-mixer when trained on medium-scale datasets and performs as well as ViT on ImageNet-1K dataset with a simpler architecture and fewer computational resources.

2022 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2022) (2022)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

UNETR: Transformers for 3D Medical Image Segmentation

Ali Hatamizadeh et al.

Summary: Fully Convolutional Neural Networks (FCNNs) have been successful in medical image segmentation, but their limited ability to learn long-range dependencies is a challenge. Inspired by transformers in NLP, we propose a novel architecture called UNet Transformers (UNETR) to redefine volumetric medical image segmentation as a sequence prediction problem. By combining transformers and U-shaped network design in the encoder and decoder, we effectively capture global information and achieve semantic segmentation output.

2022 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2022) (2022)

Add to Collection

Proceedings Paper Computer Science, Artificial Intelligence

Medical Transformer: Gated Axial-Attention for Medical Image Segmentation

Jeya Maria Jose Valanarasu et al.

Summary: Deep convolutional neural networks have been widely adopted in medical image segmentation, but lack understanding of long-range dependencies due to inherent biases in convolutional architectures. Transformer-based architectures leverage self-attention mechanism to encode long-range dependencies, motivating the exploration of transformer solutions for medical image segmentation tasks.

MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT I (2021)

Add to Collection

Article Multidisciplinary Sciences