Transformers in medical imaging: A survey
Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …
successfully applied to several computer vision problems, achieving state-of-the-art results …
Recent advances and clinical applications of deep learning in medical image analysis
Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …
processing algorithms, and deep learning based models have been remarkably successful …
Convolutions die hard: Open-vocabulary segmentation with single frozen convolutional clip
Open-vocabulary segmentation is a challenging task requiring segmenting and recognizing
objects from an open set of categories in diverse environments. One way to address this …
objects from an open set of categories in diverse environments. One way to address this …
Oneformer: One transformer to rule universal image segmentation
Abstract Universal Image Segmentation is not a new concept. Past attempts to unify image
segmentation include scene parsing, panoptic segmentation, and, more recently, new …
segmentation include scene parsing, panoptic segmentation, and, more recently, new …
Maxvit: Multi-axis vision transformer
Transformers have recently gained significant attention in the computer vision community.
However, the lack of scalability of self-attention mechanisms with respect to image size has …
However, the lack of scalability of self-attention mechanisms with respect to image size has …
Scaling up your kernels to 31x31: Revisiting large kernel design in cnns
We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by
recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a few …
recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a few …
Unext: Mlp-based rapid medical image segmentation network
JMJ Valanarasu, VM Patel - … conference on medical image computing and …, 2022 - Springer
UNet and its latest extensions like TransUNet have been the leading medical image
segmentation methods in recent years. However, these networks cannot be effectively …
segmentation methods in recent years. However, these networks cannot be effectively …
V2x-vit: Vehicle-to-everything cooperative perception with vision transformer
In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to
improve the perception performance of autonomous vehicles. We present a robust …
improve the perception performance of autonomous vehicles. We present a robust …
[HTML][HTML] Highly accurate protein structure prediction with AlphaFold
Proteins are essential to life, and understanding their structure can facilitate a mechanistic
understanding of their function. Through an enormous experimental effort 1, 2, 3, 4, the …
understanding of their function. Through an enormous experimental effort 1, 2, 3, 4, the …
SegFormer: Simple and efficient design for semantic segmentation with transformers
We present SegFormer, a simple, efficient yet powerful semantic segmentation framework
which unifies Transformers with lightweight multilayer perceptron (MLP) decoders …
which unifies Transformers with lightweight multilayer perceptron (MLP) decoders …