# Attention-Based Two-Branch Hybrid Fusion Network for Medical Image Segmentation
## 1 Introduction  
Malignant tumors, commonly referred to as cancer, are serious illnesses that pose the greatest threat to human health, second only to cardiovascular and cerebrovascular conditions [1]. The International Agency for Research on Cancer (IARC), a branch of the World Health Organization, released the latest global cancer statistics for 2020. These figures revealed that there were approximately 19.29 million new cases of cancer worldwide, with cancer causing approximately 9.96 million deaths. The incidence and mortality rates of cancer in China rank among the highest globally [2]. Approximately 4.57 million new cases are reported annually, accounting for 23.7% of the world's total new diagnoses, while over 3 million fatalities are recorded, making up 30.14% of global cancer-related deaths. Nearly half of the patients in China have been diagnosed in the middle and late stages, and even after radical surgery, about 50% of the patients will still have recurrence and metastasis. Thus, the crucial aspect in combating cancer is the timely identification and prompt management of the disease, which can substantially enhance the likelihood of patient survival. As modern medical practices continue to evolve, the prevalent method for the early detection of cancer involves the examination of histopathologic sections. This technique is considered the gold standard for cancer diagnosis within contemporary medicine [3]. Identifying and analyzing cell nuclei in histopathological sections allows for the detection of serious diseases like cancer during the early clinical stage or even the pre-clinical phase. This allows us ample time to take appropriate preventative action. However, traditional pathology analysis is performed by pathologists who analyze the morphology of specific tissue cells through a microscope and use their own experience to diagnose the sections. The large number of tissue cells contained in a pathology sample, the high degree of cellular similarity, and the small field of view of the microscope make it take a lot of time to fully diagnose a section, which seriously hinders the diagnostic efficiency. The progress in deep learning has led to Convolutional Neural Networks (CNN) becoming increasingly pivotal for medical image segmentation tasks. The automated feature extraction, robust adaptability, and quick processing speed of convolutional neural networks allow them to efficiently overcome the drawbacks of conventional detection techniques and enhance the precision and stability of medical picture segmentation [4; 5]. The field of image segmentation was introduced to encoder and decoder architectures by [PERSON] et al. [6]. They introduced the Fully Convolutional Network (FCN), pioneering an end-to-end semantic segmentation approach for images and marking the initial deep learning application in this domain. [PERSON] et al. [7] introduced the U-Net architecture, an extension of FCN that facilitates the multi-scale extraction of both superficial and profound image features during the encoding phase. To mitigate the loss of fine-grained details due to consecutive downsampling, skip connections are incorporated to link the encoder and decoder at corresponding stages, enabling the network to harness features at varied levels of granularity. Numerous derivatives of the U-Net model have been developed, capitalizing on its exceptional segmentation capabilities. These models include UNet++ [8], Res-Unet [9], Attention U-Net [10], DenseUNet [11], R2U-Net [12], KiU-Net [13], and UNet 3+ [14]. They are made especially to achieve emotive performance and segment medical images. Despite the notable success of convolutional neural networks in medical imaging, their inherent inductive bias means each convolutional kernel is limited to processing a local region of the image. This limitation hampers the network's capacity to model extensive context and form extended dependencies.