Medical image segmentation is a relevant problem, with deep learning being an
exponent. However, the necessity of a high volume of fully annotated images for
training massive models can be a problem, especially for applications whose
images present a great diversity, such as brain tumors, which can occur in
different sizes and shapes. In contrast, a recent methodology, Feature Learning
from Image Markers (FLIM), has involved an expert in the learning loop,
producing small networks that require few images to train the convolutional
layers. In this work, We employ an interactive method for image selection and
training based on FLIM, exploring the user's knowledge. The results
demonstrated that with our methodology, we could choose a small set of images
to train the encoder of a U-shaped network, obtaining performance equal to
manual selection and even surpassing the same U-shaped network trained with
backpropagation and all training images.

通过使用基于图像标记的特征学习方法，我们能够选择一小组图像来训练 U 形网络的编码器，从而达到与手动选择相等甚至超过使用反向传播和所有训练图像训练的同一 U 形网络的性能。

脑肿瘤分割网络的交互式图像选择和训练

Interactive Image Selection and Training for Brain Tumor Segmentation  Network

Denoising hyperspectral images (HSIs) is a crucial preprocessing procedure
due to the noise originating from intra-imaging mechanisms and environmental
factors. Utilizing domain-specific knowledge of HSIs, such as spectral
correlation, spatial self-similarity, and spatial-spectral correlation, is
essential for deep learning-based denoising. Existing methods are often
constrained by running time, space complexity, and computational complexity,
employing strategies that explore these priors separately. While the strategies
can avoid some redundant information, considering that hyperspectral images are
3-D images with strong spatial continuity and spectral correlation, this kind
of strategy inevitably overlooks subtle long-range spatial-spectral information
that positively impacts image restoration. This paper proposes a
Spatial-Spectral Selective State Space Model-based U-shaped network, termed
Spatial-Spectral U-Mamba (SSUMamba), for hyperspectral image denoising. We can
obtain complete global spatial-spectral correlation within a module thanks to
the linear space complexity in State Space Model (SSM) computations. We
introduce an Alternating Scan (SSAS) strategy for HSI data, which helps model
the information flow in multiple directions in 3-D HSIs. Experimental results
demonstrate that our method outperforms several compared methods. The source
code will be available at this https URL

本论文提出了一种基于空间 - 谱选择性状态空间模型的 U 型网络（称为 SSUMamba），用于高光谱图像去噪，能够在状态空间模型计算中实现完全的全局空间 - 谱相关性，通过引入交替扫描策略（SSAS）对多方向 3D 高光谱图像进行信息流建模。实验证明，我们的方法优于其他方法。

SSUMamba: 高光谱图像去噪的空间 - 光谱选择性状态空间模型

SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral  Image Denoising

The accurate segmentation of medical images is critical for various
healthcare applications. Convolutional neural networks (CNNs), especially Fully
Convolutional Networks (FCNs) like U-Net, have shown remarkable success in
medical image segmentation tasks. However, they have limitations in capturing
global context and long-range relations, especially for objects with
significant variations in shape, scale, and texture. While transformers have
achieved state-of-the-art results in natural language processing and image
recognition, they face challenges in medical image segmentation due to image
locality and translational invariance issues. To address these challenges, this
paper proposes an innovative U-shaped network called BEFUnet, which enhances
the fusion of body and edge information for precise medical image segmentation.
The BEFUnet comprises three main modules, including a novel Local
Cross-Attention Feature (LCAF) fusion module, a novel Double-Level Fusion (DLF)
module, and dual-branch encoder. The dual-branch encoder consists of an edge
encoder and a body encoder. The edge encoder employs PDC blocks for effective
edge information extraction, while the body encoder uses the Swin Transformer
to capture semantic information with global attention. The LCAF module
efficiently fuses edge and body features by selectively performing local
cross-attention on features that are spatially close between the two
modalities. This local approach significantly reduces computational complexity
compared to global cross-attention while ensuring accurate feature matching.
BEFUnet demonstrates superior performance over existing methods across various
evaluation metrics on medical image segmentation datasets.

本文提出了一种名为 BEFUnet 的创新 U 形网络，它通过增强融合身体和边缘信息来实现精确的医学图像分割，该方法在医学图像分割数据集上显示出优于现有方法的卓越性能。

BEFUnet: 用于精确医学图像分割的混合 CNN-Transformer 架构

BEFUnet: A Hybrid CNN-Transformer Architecture for Precise Medical Image  Segmentation

Document-level relation extraction aims to extract relations among multiple
entity pairs from a document. Previously proposed graph-based or
transformer-based models utilize the entities independently, regardless of
global information among relational triples. This paper approaches the problem
by predicting an entity-level relation matrix to capture local and global
information, parallel to the semantic segmentation task in computer vision.
Herein, we propose a Document U-shaped Network for document-level relation
extraction. Specifically, we leverage an encoder module to capture the context
information of entities and a U-shaped segmentation module over the image-style
feature map to capture global interdependency among triples. Experimental
results show that our approach can obtain state-of-the-art performance on three
benchmark datasets DocRED, CDR, and GDA.

本研究提出基于 Entity-level Relation Matrix 与 U-shaped Network 的 Document-level Relation Extraction 模型，通过结合上下文信息以及全局关系，实现了在 DocRED，CDR 和 GDA 三个基准数据集上的最优性能。