The Resolution of feature maps is critical for medical image segmentation.
Most of the existing Transformer-based networks for medical image segmentation
are U-Net-like architecture that contains an encoder that utilizes a sequence
of Transformer blocks to convert the input medical image from high-resolution
representation into low-resolution feature maps and a decoder that gradually
recovers the high-resolution representation from low-resolution feature maps.
Unlike previous studies, in this paper, we utilize the network design style
from the High-Resolution Network (HRNet), replace the convolutional layers with
Transformer blocks, and continuously exchange information from the different
resolution feature maps that are generated by Transformer blocks. The newly
Transformer-based network presented in this paper is denoted as High-Resolution
Swin Transformer Network (HRSTNet). Extensive experiments illustrate that
HRSTNet can achieve comparable performance with the state-of-the-art
Transformer-based U-Net-like architecture on Brain Tumor Segmentation(BraTS)
2021 and the liver dataset from Medical Segmentation Decathlon. The code of
HRSTNet will be publicly available at this https URL.

本研究提出了一种基于 Transformer 的医学图像分割网络 HRSTNet，其采用具有高分辨率的设计风格，用 Transformer 块代替卷积层，不断交换不同分辨率的特征图集来提高性能

用于自动医学图像分割的高分辨率 Swin Transformer

High-Resolution Swin Transformer for Automatic Medical Image Segmentation

Accurately segmenting a variety of clinically significant lesions from whole
body computed tomography (CT) scans is a critical task on precision oncology
imaging, denoted as universal lesion segmentation (ULS). Manual annotation is
the current clinical practice, being highly time-consuming and inconsistent on
tumor's longitudinal assessment. Effectively training an automatic segmentation
model is desirable but relies heavily on a large number of pixel-wise labelled
data. Existing weakly-supervised segmentation approaches often struggle with
regions nearby the lesion boundaries. In this paper, we present a novel
weakly-supervised universal lesion segmentation method by building an attention
enhanced model based on the High-Resolution Network (HRNet), named AHRNet, and
propose a regional level set (RLS) loss for optimizing lesion boundary
delineation. AHRNet provides advanced high-resolution deep image features by
involving a decoder, dual-attention and scale attention mechanisms, which are
crucial to performing accurate lesion segmentation. RLS can optimize the model
reliably and effectively in a weakly-supervised fashion, forcing the
segmentation close to lesion boundary. Extensive experimental results
demonstrate that our method achieves the best performance on the publicly
large-scale DeepLesion dataset and a hold-out test set.

该论文提出了一种基于 HRNet 的弱监督方法，称为 AHRNet，通过区域级别集合（RLS）损失进行优化，可实现精准的通用病变分割，实验证明在公共数据集 Deeplesion 上取得了最佳表现。

具有区域级集损失的弱监督通用病变分割

Weakly-Supervised Universal Lesion Segmentation with Regional Level Set  Loss

Neural-networks based image restoration methods tend to use low-resolution
image patches for training. Although higher-resolution image patches can
provide more global information, state-of-the-art methods cannot utilize them
due to their huge GPU memory usage, as well as the instable training process.
However, plenty of studies have shown that global information is crucial for
image restoration tasks like image demosaicing and enhancing. In this work, we
propose a HighEr-Resolution Network (HERN) to fully learning global information
in high-resolution image patches. To achieve this, the HERN employs two
parallel paths to learn image features in two different resolutions,
respectively. By combining global-aware features and multi-scale features, our
HERN is able to learn global information with feasible GPU memory usage.
Besides, we introduce a progressive training method to solve the instability
issue and accelerate model convergence. On the task of image demosaicing and
enhancing, our HERN achieves state-of-the-art performance on the AIM2019 RAW to
RGB mapping challenge. The source code of our implementation is available at
this https URL

提出了一种高分辨率神经网络（HERN）方法，通过两个并行路径来学习两个不同分辨率下的图像特征，结合全局感知特征和多尺度特征，使用渐进训练方法处理图像去马赛克和增强任务，实现了令人满意的 GPU 内存使用和较快的模型收敛。在 AIM2019 RAW 转 RGB 挑战中取得了最先进的表现。

图像去马赛克和增强的高分辨率网络

HighEr-Resolution Network for Image Demosaicing and Enhancing

High-resolution representations are essential for position-sensitive vision
problems, such as human pose estimation, semantic segmentation, and object
detection. Existing state-of-the-art frameworks first encode the input image as
a low-resolution representation through a subnetwork that is formed by
connecting high-to-low resolution convolutions \emph{in series} (e.g., ResNet,
VGGNet), and then recover the high-resolution representation from the encoded
low-resolution representation. Instead, our proposed network, named as
High-Resolution Network (HRNet), maintains high-resolution representations
through the whole process. There are two key characteristics: (i) Connect the
high-to-low resolution convolution streams \emph{in parallel}; (ii) Repeatedly
exchange the information across resolutions. The benefit is that the resulting
representation is semantically richer and spatially more precise. We show the
superiority of the proposed HRNet in a wide range of applications, including
human pose estimation, semantic segmentation, and object detection, suggesting
that the HRNet is a stronger backbone for computer vision problems. All the
codes are available at~{https://github.com/HRNet}.

High-Resolution Network (HRNet) is a new computer vision framework that maintains high-resolution representations through the whole process, resulting in semantically richer and spatially more precise representations, which outperforms existing state-of-the-art frameworks in human pose estimation, semantic segmentation, and object detection.