Segmentation Performance

Segmentation performance, the accuracy of delineating objects or regions within images, is a critical area of research across diverse fields, aiming to improve the precision and efficiency of automated image analysis. Current research focuses on enhancing existing architectures like U-Net and incorporating transformers, large language models, and foundation models like SAM to improve segmentation accuracy, particularly in challenging domains such as medical imaging and microscopy. These advancements are crucial for improving diagnostic accuracy in healthcare, accelerating scientific discovery in various biological fields, and enabling more robust automation in numerous applications. Significant effort is also being devoted to addressing challenges like noisy labels, domain adaptation, and computational efficiency.

Papers

April 30, 2022

Elucidating Meta-Structures of Noisy Labels in Semantic Segmentation by Deep Neural Networks
Yaoru Luo, Guole Liu, Yuanhao Guo, Ge Yang
Deep Neural Network Semantic Segmentation Noisy Label Segmentation Performance Pixel Level Segmentation Label Meta Structure

April 18, 2022

Inductive Biases for Object-Centric Representations in the Presence of Complex Textures
Samuele Papa, Ole Winther, Andrea Dittadi
Inductive Bias Segmentation Performance Speech Presence Object Centric Representation Segmentation Quality Neural Style Transfer Deep Texture Compound Figure Separation

April 11, 2022

Segmentation Network with Compound Loss Function for Hydatidiform Mole Hydrops Lesion Recognition
Chengze Zhu, Pingge Hu, Xianxu Zeng, Xingtong Wang, Zehua Ji, Li Shi
Segmentation Performance Segmentation Network Pathology Image Semantic Segmentation Network Hydatidiform Mole

April 5, 2022

Federated Cross Learning for Medical Image Segmentation
Xuanang Xu, Hannah H. Deng, Tianyi Chen, Tianshu Kuang, Joshua C. Barber, Daeseung Kim, Jaime Gateno, James J. Xia, Pingkun Yan
Medical Image Segmentation Segmentation Performance Federated Adversarial Cross Inferential Network

March 18, 2022

Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation
Ruihuang Li, Shuai Li, Chenhang He, Yabin Zhang, Xu Jia, Lei Zhang
Semantic Segmentation Segmentation Model Segmentation Performance Domain Adaptive Semantic Segmentation Intrinsic Image

March 16, 2022

Hyperbolic Uncertainty Aware Semantic Segmentation
Bike Chen, Wei Peng, Xiaofeng Cao, Juha Röning
Semantic Segmentation Segmentation Performance Accuracy Loss Classified Pixel

February 16, 2022

Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training
Yi Lin, Zhiyong Qu, Hao Chen, Zhongke Gao, Yuexiang Li, Lili Xia, Kai Ma, Yefeng Zheng, Kwang-Ting Cheng
Self Supervised Learning Segmentation Performance Digital Pathology Pixel Level Pathology Image Nucleus Segmentation Point Annotation Hematoxylin and Eosin

February 2, 2022

DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation
Qing Xu, Zhicheng Ma, Na HE, Wenting Duan
Convolutional Neural Network Medical Image Segmentation Segmentation Performance U Net Biomedical Image Segmentation Deep Depth DCL Net

January 7, 2022

Effect of Prior-based Losses on Segmentation Performance: A Benchmark
Rosana El Jurdi, Caroline Petitjean, Veronika Cheplygina, Paul Honeine, Fahed Abdallah
New Benchmark Medical Image Segmentation Mixed Effect Segmentation Performance Prior Based Loss

December 28, 2021

SECP-Net: SE-Connection Pyramid Network of Organ At Risk Segmentation for Nasopharyngeal Carcinoma
Zexi Huang, Lihua Guo, Xin Yang, Sijuan Huang
Segmentation Performance Medical Image Segmentation Task Pyramid Network Human Organ Risk Segmentation Nasopharyngeal Carcinoma Organ at Risk

December 21, 2021

Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation
Tariq M. Khan, Syed S. Naqvi, Erik Meijering
Medical Image Segmentation Deep Network Segmentation Performance Image Complexity

December 14, 2021

E-CRF: Embedded Conditional Random Field for Boundary-caused Class Weights Confusion in Semantic Segmentation
Jie Zhu, Huabin Huang, Banghuai Li, Leye Wang
Semantic Segmentation Segmentation Model Segmentation Performance Conditional Random Field Fully Connected CRFs Class Weight

December 9, 2021

December 6, 2021

Diffusion Models for Implicit Image Segmentation Ensembles
Julia Wolleb, Robin Sandkühler, Florentin Bieder, Philippe Valmaggia, Philippe C. Cattin
Diffusion Model Semantic Segmentation Segmentation Performance Brain Tumor Segmentation State of the Art Segmentation

November 25, 2021

Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation
Binhui Xie, Longhui Yuan, Shuang Li, Chi Harold Liu, Xinjing Cheng
Semantic Segmentation Active Learning Segmentation Performance Prediction Uncertainty Domain Adaptive Semantic Segmentation Image Region Limited Annotation Segmentation Datasets

November 18, 2021

Interactive segmentation using U-Net with weight map and dynamic user interactions
Ragavie Pirabaharan, Naimul Khan
Segmentation Performance U Net Segmentation Accuracy Interactive Segmentation Weight Re Mapping

November 16, 2021

Segmentation Performance

Papers

Elucidating Meta-Structures of Noisy Labels in Semantic Segmentation by Deep Neural Networks

Inductive Biases for Object-Centric Representations in the Presence of Complex Textures

Segmentation Network with Compound Loss Function for Hydatidiform Mole Hydrops Lesion Recognition

Federated Cross Learning for Medical Image Segmentation

Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation

Hyperbolic Uncertainty Aware Semantic Segmentation

Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training

DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation

Effect of Prior-based Losses on Segmentation Performance: A Benchmark

SECP-Net: SE-Connection Pyramid Network of Organ At Risk Segmentation for Nasopharyngeal Carcinoma

Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation

E-CRF: Embedded Conditional Random Field for Boundary-caused Class Weights Confusion in Semantic Segmentation

Exploring Event-driven Dynamic Context for Accident Scene Segmentation

CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation

Diffusion Models for Implicit Image Segmentation Ensembles

Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation

Interactive segmentation using U-Net with weight map and dynamic user interactions

Diversified Multi-prototype Representation for Semi-supervised Segmentation

FedCostWAvg: A new averaging for better Federated Learning