Paper Reading AI Learner

PL-Net: Progressive Learning Network for Medical Image Segmentation

2021-10-27 14:57:05
Junlong Cheng, Chengrui Gao, Chaoqing Wang, Zhangqiang Ming, Yong Yang, Min Zhu

Abstract

In recent years, segmentation methods based on deep convolutional neural networks (CNNs) have made state-of-the-art achievements for many medical analysis tasks. However, most of these approaches improve performance by optimizing the structure or adding new functional modules of the U-Net, which ignoring the complementation and fusion of the coarse-grained and fine-grained semantic information. To solve the above problems, we propose a medical image segmentation framework called progressive learning network (PL-Net), which includes internal progressive learning (IPL) and external progressive learning (EPL). PL-Net has the following advantages: (1) IPL divides feature extraction into two "steps", which can mix different size receptive fields and capture semantic information from coarse to fine granularity without introducing additional parameters; (2) EPL divides the training process into two "stages" to optimize parameters, and realizes the fusion of coarse-grained information in the previous stage and fine-grained information in the latter stage. We evaluate our method in different medical image analysis tasks, and the results show that the segmentation performance of PL-Net is better than the state-of-the-art methods of U-Net and its variants.

Abstract (translated)

URL

https://arxiv.org/abs/2110.14484

PDF

https://arxiv.org/pdf/2110.14484.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot