Accuracy Improvement of Cell Image Segmentation Using Feedback Former
Meijo University（名古屋大学）
Hinako Mitsuoka, Kazuhiro Hotta
备注：
Accepted by ECCV2024 Workshop "Human-inspired Computer Vision (HCV)". 2025/3/19 : An extended version of this paper has been accepted for publication in IEEE Access. The published version is available at DOI: https://doi.org/10.1109/ACCESS.2025.3552847
Semantic segmentation of microscopy cell images by deep learning is a significant technique. We considered that the Transformers, which have recently outperformed CNNs in image recognition, could also be improved and developed for cell image segmentation. Transformers tend to focus more on contextual information than on detailed information. This tendency leads to a lack of detailed information for segmentation. Therefore, to supplement or reinforce the missing detailed information, we hypothesized that feedback processing in the human visual cortex should be effective. Our proposed Feedback Former is a novel architecture for semantic segmentation, in which Transformers is used as an encoder and has a feedback processing mechanism. Feature maps with detailed information are fed back to the lower layers from near the output of the model to compensate for the lack of detailed information which is the weakness of Transformers and improve the segmentation accuracy. By experiments on three cell image datasets, we confirmed that our method surpasses methods without feedback, demonstrating its superior accuracy in cell image segmentation. Our method achieved higher segmentation accuracy while consuming less computational cost than conventional feedback approaches. Moreover, our method offered superior precision without simply increasing the model size of Transformer encoder, demonstrating higher accuracy with lower computational cost.
MP-Former: Mask-Piloted Transformer for Image Segmentation
The Hong Kong University of Science and Technology（香港科技大学）；The Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）；International Digital Economy Academy (IDEA)（国际数字经济研究院（IDEA））；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Feedback Chain Network For Hippocampus Segmentation
Tiangong University（天津工业大学）；Beijing Jiaotong University（北京交通大学）；Huawei Technologies（华为技术有限公司）；City University of Hong Kong（香港城市大学）；City University of Hong Kong Shenzhen Research Institute（香港城市大学深圳研究院）
DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation
RWTH Aachen University（亚琛工业大学）；Shahid Beheshti University（沙希德·贝赫什提大学）；Iran University of Science and Technology（伊朗科技大学）；University of Regensburg（雷根斯堡大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）
SegMix:Shuffle-based Feedback Learning for Semantic Segmentation of Pathology Images
JD AI Research（京东人工智能研究院）；School of Microelectronics, Xi’an Jiaotong University（西安交通大学微电子学院）；Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University（北京生物医学工程先进创新中心，北京航空航天大学生物科学与医学工程学院）
Per-Pixel Feedback for improving Semantic Segmentation
Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校）
语义分割是为图像中的每个像素分配标签的任务，近年来，深度卷积神经网络一直在推动与认知相关的多个任务的进步。虽然DCNN带来了前所未有的视觉识别性能，但它们几乎没有透明度。为了理解基于DCNN的模型如何在语义分割任务中工作，我们尝试分析语义分割中的DCNN模型。我们试图找到全局图像信息标记像素的重要性。基于对区分区域的实验和注视点的建模，我们提出了一组新的训练损失函数，用于微调基于DCNN的模型。所提出的训练方案已经显示出针对PASCAL VOC 2012数据集的DeepLab Large FOV（VGG-16）分割模型的性能改进。然而，仍需进一步测试，以最终评价拟议的损失函数在模型和数据集上的受益。提交的部分履行应用数学科学综合硕士学位的要求。更新：进一步的实验显示出最小的好处。代码可用[这里]（https：//github.com/BardOfCodes/Seg-Unravel）.
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
SimulaMet（SimulaMet研究所）；UiT The Arctic University of Norway（挪威北极大学）；University of Oxford（牛津大学）；Oslo Metropolitan University（奥斯陆城市大学）；Oxford NIHR Biomedical Research Centre（牛津NIHR生物医学研究中心）
Deep Learning on 3D Semantic Segmentation: A Detailed Review
National Technical University of Athens (NTUA)（雅典国立技术大学）；Université de Strasbourg（斯特拉斯堡大学）；INSA Strasbourg（斯特拉斯堡国立应用科学学院）；CNRS（法国国家科学研究中心）；ICube Laboratory（ICube实验室）
Laplacian-Former: Overcoming the Limitations of Vision Transformers in Local Texture Detection
RWTH Aachen University（亚琛工业大学）；Iran University of Science and Technology（伊朗科技大学）；South Dakota State University（南达科他州立大学）；Shahid Beheshti University（沙希德·贝赫什提大学）；Northwestern University（西北大学）；University of Regensburg（雷根斯堡大学）
FU-net: Multi-class Image Segmentation Using Feedback Weighted U-net
School of Computer Science, University of Nottingham（诺丁汉大学计算机学院）；School of Medicine, University of Nottingham（诺丁汉大学医学院）；Sir Peter Mansfield Imaging Centre, University of Nottingham（诺丁汉大学彼得·曼斯菲尔德爵士成像中心）
VISTA-PATH: An interactive foundation model for pathology image segmentation and quantitative analysis in computational pathology
Department of Pathology and Laboratory Medicine, University of Pennsylvania（病理学与实验室医学系，宾夕法尼亚大学）；Department of Electrical and System Engineering, University of Pennsylvania（电气与系统工程系，宾夕法尼亚大学）；Department of Biomedical Engineering, Georgia Institute of Technology and Emory University（生物医学工程系，佐治亚理工学院和埃默里大学）；NVIDIA Corporation（NVIDIA公司）；Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania（生物统计学、流行病学与信息学系，宾夕法尼亚大学）
School of Electronic Information, Wuhan University of Science and Technology（武汉科技大学电子信息学院）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（北京邮电大学网络与交换技术国家重点实验室）；Oriental Space Port Research Institute（东方航天港研究院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）
Plug-in Feedback Self-adaptive Attention in CLIP for Training-free Open-Vocabulary Segmentation
University of Toronto（多伦多大学）；China Agricultural University（中国农业大学）；Concordia University（Concordia大学）；McMaster University（麦马斯特大学）；Beijing Jiaotong University（北京交通大学）
GCUNet: A GNN-Based Contextual Learning Network for Tertiary Lymphoid Structure Semantic Segmentation in Whole Slide Image
Institute of Automation, Chinese Academy of Sciences (CASIA)（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS)（中国科学院大学人工智能学院）；Clinical Oncology School of Fujian Medical University（福建医科大学临床肿瘤学院）；Fujian Cancer Hospital（福建省肿瘤医院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
