Self-supervised learning has been increasingly investigated for low-dose computed tomography (LDCT) image denoising, as it alleviates the dependence on paired normal-dose CT (NDCT) data, which are often difficult to obtain. However, many existing self-supervised blind-spot denoising methods may under-utilize pixel-wise supervisory information loss due to evaluating the training loss only at masked locations. To mitigate this issue, we propose a novel Progressive Masked Refinement Learning framework that progressively refines denoising results while preserving and exploiting available LDCT information. Specifically, we explicitly inject a combination of controlled Gaussian and Poisson noise during training to regularize the denoising process and mitigate trivial identity mapping. Furthermore, we introduce a step-wise mask denoising mechanism that gradually reduces the discrepancy between synthetic corruption and the noise characteristics of LDCT images, enabling more fine-grained learning for denoising. Extensive experiments on the Mayo LDCT dataset demonstrate that the proposed method outperforms existing self-supervised approaches and achieves performance comparable to, or better than, several representative supervised denoising methods.
Low-dose computed tomography (LDCT) reduces the X-ray radiation but compromises image quality with more noises and artifacts. A plethora of transformer models have been developed recently to improve LDCT image quality. However, the success of a transformer model relies on a large amount of paired noisy and clean data, which is often unavailable in clinical applications. In computer vision and natural language processing fields, masked autoencoders (MAE) have been proposed as an effective label-free self-pretraining method for transformers, due to its excellent feature representation ability. H…
ProSAC-CT: Progressive Spectral-Anatomical Co-Guided Multi-Stage Diffusion Model for Low-Dose CT Denoising
College of Medicine and Biological Information Engineering, Northeastern University（东北大学医学与生物信息工程学院）；Tohoku University School of Medicine（东北大学医学院）；State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center（华南肿瘤学国家重点实验室，中山大学肿瘤防治中心）；AI Lab, Tohoku University Hospital（东北大学医院人工智能实验室）
MAP-Diff: Multi-Anchor Guided Diffusion for Progressive 3D Whole-Body Low-Dose PET Denoising
School of Engineering, Zurich University of Applied Sciences, CH；Bioengineering Department；Imperial-X, Imperial College London, UK；DAMTP, University of Cambridge, UK；Lucerne University Teaching；Research Hospital, CH；Lung Institute, Imperial College London, UK；Cardiovascular Research Centre, Royal Brompton Hospital, UK；School of Biomedical Engineering \& Imaging Sciences, King's College London, UK；Yau Mathematical Sciences Center, Tsinghua University, CN
Hong Kong Baptist University（香港浸会大学）；Institute of High Performance Computing, A*STAR（新加坡科技研究局高性能计算研究院）；vivo Mobile Communication Co. Ltd（维沃移动通信有限公司）；City University of Hong Kong（香港城市大学）；Nanyang Technological University（南洋理工大学）
High Dynamic Range (HDR) imaging via multi-exposure fusion is an important task for most modern imaging platforms. In spite of recent developments in both hardware and algorithm innovations, challenges remain over content association ambiguities caused by saturation, motion, and various artifacts introduced during multi-exposure fusion such as ghosting, noise, and blur. In this work, we propose an Attention-guided Progressive Neural Texture Fusion (APNT-Fusion) HDR restoration model which aims to address these issues within one framework. An efficient two-stream structure is proposed which sep…
Despite its tremendous value for the diagnosis, treatment monitoring and surveillance of children with cancer, whole body staging with positron emission tomography (PET) is time consuming and associated with considerable radiation exposure. 100x (1% of the standard clinical dosage) ultra-low-dose/ultra-fast whole-body PET reconstruction has the potential for cancer imaging with unprecedented speed and improved safety, but it cannot be achieved by the naive use of machine learning techniques. In this study, we utilize the global similarity between baseline and follow-up PET and magnetic resonan…
The University of Tokyo Hospital（东京大学医学部附属医院）；Center for Frontier Medical Engineering, Chiba University（千叶大学前沿医疗工程中心）；Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo（东京大学医学研究生院放射科与生物医学工程学系）
Ultra sparse-view computed tomography (CT) algorithms can reduce radiation exposure of patients, but those algorithms lack an explicit cycle consistency loss minimization and an explicit log-likelihood maximization in testing. Here, we propose X2CT-FLOW for the maximum a posteriori (MAP) reconstruction of a three-dimensional (3D) chest CT image from a single or a few two-dimensional (2D) projection images using a progressive flow-based deep generative model, especially for ultra low-dose protocols. The MAP reconstruction can simultaneously optimize the cycle consistency loss and the log-likeli…
Image inpainting is one of the most challenging tasks in computer vision. Recently, generative-based image inpainting methods have been shown to produce visually plausible images. However, they still have difficulties to generate the correct structures and colors as the masked region grows large. This drawback is due to the training stability issue of the generative models. This work introduces a new curriculum-style training approach in the context of image inpainting. The proposed method increases the masked region size progressively in training time, during test time the user gives variable…
MM-3DScene: 3D Scene Understanding by Customizing Masked Modeling with Informative-Preserved Reconstruction and Self-Distilled Consistency
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
How to identify and segment camouflaged objects from the background is challenging. Inspired by the multi-head self-attention in Transformers, we present a simple masked separable attention (MSA) for camouflaged object detection. We first separate the multi-head self-attention into three parts, which are responsible for distinguishing the camouflaged objects from the background using different mask strategies. Furthermore, we propose to capture high-resolution semantic representations progressively based on a simple top-down decoder with the proposed MSA to attain precise segmentation results.…
Self is the Best Learner: CT-free Ultra-Low-Dose PET Organ Segmentation via Collaborating Denoising and Segmentation Learning
School of Biomedical Engineering, Southern Medical University（生物医学工程学院，南方医科大学）；School of Biomedical Engineering, Shanghai Jiao Tong University（生物医学工程学院，上海交通大学）；Nanfang PET Center, Nanfang Hospital Southern Medical University（南芳PET中心，南方医科大学南芳医院）；Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University（广东省医学图像处理重点实验室，南方医科大学）；Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University（广东省医学影像与诊断技术工程实验室，南方医科大学）；Pazhou Lab（琶洲实验室）
Physics-Driven Independent Pair Generation for Iterative Self-Supervised Low-Dose CT Denoising
Nanchang University（南昌大学）；School of Information Engineering, Nanchang University（南昌大学信息工程学院）；School of Biomedical Engineering, Sun Yat-sen University（中山大学医学院）
CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI Synthesis
School of Medical Technology, Beijing Institute of Technology（北京理工大学医学技术学院）；School of Integrated Circuits and Electronics, Beijing Institute of Technology（北京理工大学集成电路与电子学院）
University of Sydney（悉尼大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
The combination of transformers and masked image modeling (MIM) pre-training framework has shown great potential in various vision tasks. However, the pre-training computational budget is too heavy and withholds the MIM from becoming a practical training paradigm. This paper presents FastMIM, a simple and generic framework for expediting masked image modeling with the following two steps: (i) pre-training vision backbones with low-resolution input images; and (ii) reconstructing Histograms of Oriented Gradients (HOG) feature instead of original RGB values of the input images. In addition, we p…
Progressive Self-Supervised Learning with Individualized Community Assignment for Brain Network Analysis
Harbin Institute of Technology at Shenzhen（哈尔滨工业大学（深圳））；Peng Cheng Laboratory（鹏城实验室）；University of Tübingen（图宾根大学）；Max Planck Institute for Intelligent Systems（智能系统马克斯·普朗克研究所）
The University of Melbourne（墨尔本大学）；Monash University（莫纳什大学）
Modern video summarization methods are based on deep neural networks that require a large amount of annotated data for training. However, existing datasets for video summarization are small-scale, easily leading to over-fitting of the deep models. Considering that the annotation of large-scale datasets is time-consuming, we propose a multimodal self-supervised learning framework to obtain semantic representations of videos, which benefits the video summarization task. Specifically, the self-supervised learning is conducted by exploring the semantic consistency between the videos and text in bo…
One-Step Is Optimal: Unconditional Rectified Flows are Noise2Noise Denoisers, and Multi-Step Integration Provably Hurts---A Benchmark and Task-Based Detectability Study on Low-Dose CT
OpenUS: A Fully Open-Source Foundation Model for Ultrasound Image Analysis via Self-Adaptive Masked Contrastive Learning
Digital Environment Research Institute (DERI), Queen Mary University of London（女王玛丽大学数字环境研究 institute（DERI））；The William Harvey Research Institute, Queen Mary University of London（女王玛丽大学威廉·哈维研究 institute）；School of Electronic Engineering and Computer Science, Queen Mary University of London（女王玛丽大学电子工程与计算机科学学院）
The Chinese University of Hong Kong（香港中文大学）；Shanghai AI Laboratory（上海人工智能实验室）；Centre for Perceptual and Interactive Intelligence Limited（感知与交互智能中心有限公司）
Masked Autoencoders (MAE) have shown great potentials in self-supervised pre-training for language and 2D image transformers. However, it still remains an open question on how to exploit masked autoencoding for learning 3D representations of irregular point clouds. In this paper, we propose Point-M2AE, a strong Multi-scale MAE pre-training framework for hierarchical self-supervised learning of 3D point clouds. Unlike the standard transformer in MAE, we modify the encoder and decoder into pyramid architectures to progressively model spatial geometries and capture both fine-grained and high-leve…
Hefei University of Technology（合肥工业大学）；Lenovo Research（联想研究院）
Image inpainting has achieved remarkable progress and inspired abundant methods, where the critical bottleneck is identified as how to fulfill the high-frequency structure and low-frequency texture information on the masked regions with semantics. To this end, deep models exhibit powerful superiority to capture them, yet constrained on the local spatial regions. In this paper, we delve globally into texture and structure information to well capture the semantics for image inpainting. As opposed to the existing arts trapped on the independent local patches, the texture information of each patch…
AnF-DiffPET: Anatomy- and Frequency-Guided Diffusion for PET/CT Denoising
College of Medicine and Biological Information Engineering, Northeastern University（东北大学医学与生物信息工程学院）；Tohoku University School of Medicine（东北大学医学院）；Department of Clinical Neurosciences, University of Cambridge（剑桥大学临床神经科学系）
Multi-scale Restoration of Missing Data in Optical Time-series Images with Masked Spatial-Temporal Attention Network
School of Computer Science, China University of Geosciences（中国地质大学（武汉）计算机学院）；Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software, Ministry of Education（教育部自然资源信息管理与数字孪生工程软件工程研究中心）；Second Surveying and Mapping Institute of Hunan Province（湖南省第二测绘院）；Key Laboratory of Natural Resources Monitoring and Supervision in Southern Hilly Region, Ministry of Natural Resources（自然资源部南方山地自然资源监测与监管重点实验室）
EndoVLM: An Endoscopy Vision-Language Pre-training Model via Anatomy-Guided Sparsity and Progressive Alignment
DAMO Academy, Alibaba Group（阿里巴巴达摩院）；The First Affiliated Hospital of Zhejiang Chinese Medical University（浙江中医药大学附属第一医院）；Shanghai Jiao Tong University（上海交通大学）；Hupan Lab（湖畔实验室）；Zhejiang University（浙江大学）
Growing to See: Efficient Pretraining of Self-Supervised Vision Transformers
KTH Royal Institute of Technology（皇家理工学院）；Helmholtz AI（亥姆霍兹人工智能研究所）；Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Google（谷歌）；Digital Futures（数字未来）
SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation
Center for Vision, Speech, and Signal Processing University Of Surrey Guildford, UK（视觉、语音和信号处理中心 英国萨里大学 Guildford分校）；Centre for Environmental Health and Engineering University Of Surrey Guildford, UK（环境健康与工程中心 英国萨里大学 Guildford分校）
