VIS-MAE: An Efficient Self-supervised Learning Approach on Medical Image Segmentation and Classification
BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院生物医学工程与影像研究所）；University of Oklahoma（俄克拉荷马大学）；NVIDIA（英伟达公司）；Erasmus University Rotterdam（伊拉斯姆斯大学鹿特丹分校）；East River Medical Imaging（东河医学影像中心）；Cornell Medicine（康奈尔医学院）；Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院温赖希人工智能与人类健康系）
Zelong Liu, Andrew Tieu, Nikhil Patel, Georgios Soultanidis, Louisa Deyer, Ying Wang, Sean Huver, Alexander Zhou, Yunhao Mei, Zahi A. Fayad, Timothy Deyer, Xueyan Mei
备注：
Accepted at MLMI@MICCAI (Workshop on Machine Learning in Medical Imaging at MICCAI 2024))
发表信息：
15th International Workshop, MLMI 2024, Held in Conjunction with MICCAI 2024
Artificial Intelligence (AI) has the potential to revolutionize diagnosis and segmentation in medical imaging. However, development and clinical implementation face multiple challenges including limited data availability, lack of generalizability, and the necessity to incorporate multi-modal data effectively. A foundation model, which is a large-scale pre-trained AI model, offers a versatile base that can be adapted to a variety of specific tasks and contexts. Here, we present VIsualization and Segmentation Masked AutoEncoder (VIS-MAE), novel model weights specifically designed for medical imaging. Specifically, VIS-MAE is trained on a dataset of 2.5 million unlabeled images from various modalities (CT, MR, PET,X-rays, and ultrasound), using self-supervised learning techniques. It is then adapted to classification and segmentation tasks using explicit labels. VIS-MAE has high label efficiency, outperforming several benchmark models in both in-domain and out-of-domain applications. In addition, VIS-MAE has improved label efficiency as it can achieve similar performance to other models with a reduced amount of labeled training data (50% or 80%) compared to other pre-trained weights. VIS-MAE represents a significant advancement in medical imaging AI, offering a generalizable and robust solution for improving segmentation and classification tasks while reducing the data annotation workload. The source code of this work is available at https://github.com/lzl199704/VIS-MAE.
DCELANM-Net:Medical Image Segmentation based on Dual Channel Efficient Layer Aggregation Network with Learner
School of Computer Science, Hubei University of Technology（湖北工业大学计算机学院）；Lublin University of Technology（卢布林工业大学）；Lviv Polytechnic National University（利沃夫国立理工大学）
Sun Yat-sen University（中山大学）；The Third Affiliated Hospital of Southern Medical University（南方医科大学第三附属医院）
Medical image segmentation based on deep learning is often faced with the problems of insufficient datasets and long time-consuming labeling. In this paper, we introduce the self-supervised method MAE(Masked Autoencoders) into knee joint images to provide a good initial weight for the segmentation model and improve the adaptability of the model to small datasets. Secondly, we propose a weakly supervised paradigm for meniscus segmentation based on the combination of point and line to reduce the time of labeling. Based on the weak label ,we design a region growing algorithm to generate pseudo-la…
PlankFormer: Robust Plankton Instance Segmentation via MAE-Pretrained Vision Transformers and Pseudo Community Image Generation
Graduate School of Information Sciences, Tohoku University（东京大学信息科学研究生院）；The Center for Ecological Research, Kyoto University（京都大学生态研究中心）；Graduate School of Life Sciences, Tohoku University（东京大学生命科学研究生院）
Electronics and Telecommunications Research Institute (ETRI)（电子与电信研究所）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；AITRICS
Masked image modeling (MIM) has become a popular strategy for self-supervised learning~(SSL) of visual representations with Vision Transformers. A representative MIM model, the masked auto-encoder (MAE), randomly masks a subset of image patches and reconstructs the masked patches given the unmasked patches. Concurrently, many recent works in self-supervised learning utilize the student/teacher paradigm which provides the student with an additional target based on the output of a teacher composed of an exponential moving average (EMA) of previous students. Although common, relatively little is …
Efficient Self-supervised Vision Pretraining with Local Masked Reconstruction
King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Monash University（莫纳什大学）；Nile University（尼罗河大学）；Nanyang Technological University（南洋理工大学）
Masked Autoencoding (MAE) has emerged as an effective approach for pre-training representations across multiple domains. In contrast to discrete tokens in natural languages, the input for image MAE is continuous and subject to additional specifications. We systematically study each input specification during the pre-training stage, and find sequence length is a key axis that further scales MAE. Our study leads to a long-sequence version of MAE with minimal changes to the original recipe, by just decoupling the mask size from the patch size. For object detection and semantic segmentation, our l…
University of Science and Technology of China（中国科学技术大学）；Microsoft Research Asia（微软亚洲研究院）
We propose bootstrapped masked autoencoders (BootMAE), a new approach for vision BERT pretraining. BootMAE improves the original masked autoencoders (MAE) with two core designs: 1) momentum encoder that provides online feature as extra BERT prediction targets; 2) target-aware decoder that tries to reduce the pressure on the encoder to memorize target-specific information in BERT pretraining. The first design is motivated by the observation that using a pretrained MAE to extract the features as the BERT prediction target for masked tokens can achieve better pretraining performance. Therefore, w…
MedFLIP: Medical Vision-and-Language Self-supervised Fast Pre-Training with Masked Autoencoder
University of Copenhagen（哥本哈根大学）；Tsinghua University（清华大学）；Shanghai Medical Image Insights（上海医学影像洞察）；Harvard University（哈佛大学）；University of Waterloo（滑铁卢大学）
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”（乌克兰国立技术大学“伊戈尔·西科尔斯基基辅理工学院”）
The coronavirus pandemic has been going on since the year 2019, and the trend is still not abating. Therefore, it is particularly important to classify medical CT scans to assist in medical diagnosis. At present, Supervised Deep Learning algorithms have made a great success in the classification task of medical CT scans, but medical image datasets often require professional image annotation, and many research datasets are not publicly available. To solve this problem, this paper is inspired by the self-supervised learning algorithm MAE and uses the MAE model pre-trained on ImageNet to perform …
Self-supervised models allow (pre-)training on unlabeled data and therefore have the potential to overcome the need for large annotated cohorts. One leading self-supervised model is the masked autoencoder (MAE) which was developed on natural imaging data. The MAE is masking out a high fraction of visual transformer (ViT) input patches, to then recover the uncorrupted images as a pretraining task. In this work, we extend MAE to perform anomaly detection on breast magnetic resonance imaging (MRI). This new model, coined masked autoencoder for medical imaging (MAEMI) is trained on two non-contras…
USF-MAE: Ultrasound Self-Supervised Foundation Model with Masked Autoencoding
Department of Systems and Computer Engineering, Carleton University（系统与计算机工程系，卡尔顿大学）；Department of Clinical Science and Translational Medicine, University of Ottawa（临床科学与转化医学系，渥太华大学）；Department of Obstetrics and Gynecology, University of Ottawa（妇产科系，渥太华大学）；School of Epidemiology and Public Health, University of Ottawa（流行病学与公共卫生学院，渥太华大学）；Department of Methodological and Implementation Research, Ottawa Hospital Research Institute（方法学与实施研究系，渥太华医院研究学院）；Department of Acute Care Research, Ottawa Hospital Research Institute（急症护理研究系，渥太华医院研究学院）
National University of Sciences and Technology (NUST)（国立科技大学（NUST））
Recent escalation in the field of computer vision underpins a huddle of algorithms with the magnificent potential to unravel the information contained within images. These computer vision algorithms are being practised in medical image analysis and are transfiguring the perception and interpretation of Imaging data. Among these algorithms, Vision Transformers are evolved as one of the most contemporary and dominant architectures that are being used in the field of computer vision. These are immensely utilized by a plenty of researchers to perform new as well as former experiments. Here, in thi…
E3D-GPT: Enhanced 3D Visual Foundation for Medical Vision-Language Model
University of Science and Technology of China（中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏州高等研究院）；iFlytek Co.Ltd（科大讯飞股份有限公司）；The First Affiliated of USTC, Anhui Provincial Hospital（中国科学技术大学附属第一医院（安徽省立医院））
University of Pittsburgh（匹兹堡大学）；University of Notre Dame（圣母大学）；George Mason University（乔治梅森大学）；University of Pittsburgh Medical Center（匹兹堡大学医学中心）
In dermatological disease diagnosis, the private data collected by mobile dermatology assistants exist on distributed mobile devices of patients. Federated learning (FL) can use decentralized data to train models while keeping data local. Existing FL methods assume all the data have labels. However, medical data often comes without full labels due to high labeling costs. Self-supervised learning (SSL) methods, contrastive learning (CL) and masked autoencoders (MAE), can leverage the unlabeled data to pre-train models, followed by fine-tuning with limited labels. However, combining SSL and FL h…
