The Masked Autoencoder (MAE) has recently demonstrated effectiveness in pre-training Vision Transformers (ViT) for analyzing natural images. By reconstructing complete images from partially masked inputs, the ViT encoder gathers contextual information to predict the missing regions. This capability to aggregate context is especially important in medical imaging, where anatomical structures are functionally and mechanically linked to surrounding regions. However, current methods do not consider variations in the number of input images, which is typically the case in real-world Magnetic Resonance (MR) studies. To address this limitation, we propose a 3D Adaptive Masked Autoencoders (AMAE) architecture that accommodates a variable number of 3D input contrasts per subject. A magnetic resonance imaging (MRI) dataset of 45,364 subjects was used for pretraining and a subset of 1648 training, 193 validation and 215 test subjects were used for finetuning. The performance demonstrates that self pre-training of this adaptive masked autoencoders can enhance the infarct segmentation performance by 2.8%-3.7% for ViT-based segmentation models.
Medical images can be a valuable resource for reliable information to support medical diagnosis. However, the large volume of medical images makes it challenging to retrieve relevant information given a particular scenario. To solve this challenge, content-based image retrieval (CBIR) attempts to characterize images (or image regions) with invariant content information in order to facilitate image search. This work presents a feature extraction technique for medical images using stacked autoencoders, which encode images to binary vectors. The technique is applied to the IRMA dataset, a collect…
Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances. More recently, having outperformed all conventional methods, deep learning based models have shown a great promise. These methods are however limited for requirement of large training sample size and high computational costs. In this paper we show that using small sample size, denoising autoencoders constructed using convolutional layers can be used for efficient denoising of medical images. Heterogeneous images can be…
Technical University of Munich（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Inpainting has recently been proposed as a successful deep learning technique for unsupervised medical image model discovery. The masks used for inpainting are generally independent of the dataset and are not tailored to perform on different given classes of anatomy. In this work, we introduce a method for generating shape-aware masks for inpainting, which aims at learning the statistical shape prior. We hypothesize that although the variation of masks improves the generalizability of inpainting models, the shape of the masks should follow the topology of the organs of interest. Hence, we prop…
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 Institute of Neurological Disorders and Stroke（美国国家神经疾病与中风研究所）；University of Houston（休斯顿大学）
Reliable large-scale cell detection and segmentation is the fundamental first step to understanding biological processes in the brain. The ability to phenotype cells at scale can accelerate preclinical drug evaluation and system-level brain histology studies. The impressive advances in deep learning offer a practical solution to cell image detection and segmentation. Unfortunately, categorizing cells and delineating their boundaries for training deep networks is an expensive process that requires skilled biologists. This paper presents a novel self-supervised Dual-Loss Adaptive Masked Autoenco…
CMViM: Contrastive Masked Vim Autoencoder for 3D Multi-modal Representation Learning for AD classification
The Hong Kong Polytechnic University（香港理工大学）；University of Electronic Science and Technology of China（电子科技大学）；Shandong University（山东大学）；Research Institute for Smart Ageing, The Hong Kong Polytechnic University（香港理工大学智能老龄化研究院）
The development of deep learning models in medical image analysis is majorly limited by the lack of large-sized and well-annotated datasets. Unsupervised learning does not require labels and is more suitable for solving medical image analysis problems. However, most of the current unsupervised learning methods need to be applied to large datasets. To make unsupervised learning applicable to small datasets, we proposed Swin MAE, which is a masked autoencoder with Swin Transformer as its backbone. Even on a dataset of only a few thousand medical images and without using any pre-trained models, S…
Philips Research（飞利浦研究院）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Goethe University Frankfurt（法兰克福歌德大学）
Anomaly detection is the problem of recognizing abnormal inputs based on the seen examples of normal data. Despite recent advances of deep learning in recognizing image anomalies, these methods still prove incapable of handling complex medical images, such as barely visible abnormalities in chest X-rays and metastases in lymph nodes. To address this problem, we introduce a new powerful method of image anomaly detection. It relies on the classical autoencoder approach with a re-designed training pipeline to handle high-resolution, complex images and a robust way of computing an image abnormalit…
Tissue-Contrastive Semi-Masked Autoencoders for Segmentation Pretraining on Chest CT
Hangzhou City University（杭州城市大学）；Southwest Hospital, Army Medical University (Third Military Medical University)（陆军军医大学（第三军医大学）西南医院）；Zhejiang University（浙江大学）
Eindhoven University of Technology（埃因霍温理工大学）；Philips GmbH Innovative Technologies（飞利浦创新技术有限公司）
Automatic segmentation of anatomical structures with convolutional neural networks (CNNs) constitutes a large portion of research in medical image analysis. The majority of CNN-based methods rely on an abundance of labeled data for proper training. Labeled medical data is often scarce, but unlabeled data is more widely available. This necessitates approaches that go beyond traditional supervised learning and leverage unlabeled data for segmentation tasks. This work investigates the potential of autoencoder-extracted features to improve segmentation with a CNN. Two strategies were considered. F…
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…
Resource-Efficient Multiview Perception: Integrating Semantic Masking with Masked Autoencoders
Faculty of Engineering, The University of Sydney, Australia（悉尼大学工程学院）；School of Computer, Data & Mathematical Sciences, Western Sydney University, Australia（西悉尼大学计算机、数据与数学科学学院）
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…
A New Multimodal Medical Image Fusion based on Laplacian Autoencoder with Channel Attention
Visvesvaraya National Institute of Technology Nagpur（那格浦尔维斯瓦拉亚国家理工学院）；Universitat de Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；All India Institute of Medical Sciences Nagpur（那格浦尔全印医学科学学院）
Indian Institute of Information Technology Allahabad（印度阿拉哈巴德信息技术学院）
In this work, we propose a two-stage autoencoder based compressor-decompressor framework for compressing malaria RBC cell image patches. We know that the medical images used for disease diagnosis are around multiple gigabytes size, which is quite huge. The proposed residual-based dual autoencoder network is trained to extract the unique features which are then used to reconstruct the original image through the decompressor module. The two latent space representations (first for the original image and second for the residual image) are used to rebuild the final original image. Color-SSIM has be…
Indraprastha Institute of Information Technology（因德拉普拉斯信息技术学院）
In this work we address the problem of real-time dynamic medical MRI and X Ray CT image reconstruction from parsimonious samples Fourier frequency space for MRI and sinogram tomographic projections for CT. Today the de facto standard for such reconstruction is compressed sensing. CS produces high quality images (with minimal perceptual loss, but such reconstructions are time consuming, requiring solving a complex optimization problem. In this work we propose to learn the reconstruction from training samples using an autoencoder. Our work is based on the universal function approximation capacit…
