IEE Transactions on Medical Imaging This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.

A Hierarchical Graph V-Net With Semi-Supervised Pre-Training for Histological Image Based Breast Cancer Classification
Numerous patch-based methods have recently been proposed for histological image based breast cancer classification. However, their performance could be highly affected by ignoring spatial contextual information in the whole slide image (WSI). To address this issue, we propose a novel hierarchical Graph V-Net by integrating 1) patch-level pre-training and 2) context-based fine-tuning, with a hierarchical graph network. Specifically, a semi-supervised framework based on knowledge distillation is first developed to pre-train a patch encoder for extracting disease-relevant features. Then, a hierarchical Graph V-Net is designed...
Source: IEE Transactions on Medical Imaging - September 19, 2023 Category: Biomedical Engineering Source Type: research

Phase Aberration Correction for In Vivo Ultrasound Localization Microscopy Using a Spatiotemporal Complex-Valued Neural Network
Ultrasound Localization Microscopy (ULM) can map microvessels at a resolution of a few micrometers ( $\mu \text{m}$ ). Transcranial ULM remains challenging in presence of aberrations caused by the skull, which lead to localization errors. Herein, we propose a deep learning approach based on recently introduced complex-valued convolutional neural networks (CV-CNNs) to retrieve the aberration function, which can then be used to form enhanced images using standard delay-and-sum beamforming. CV-CNNs were selected as they can apply time delays through multiplication with in-phase quadrature input data. Predicting the aberration...
Source: IEE Transactions on Medical Imaging - September 18, 2023 Category: Biomedical Engineering Source Type: research

AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge
The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in real-world scenarios due to the presence of out-of-distribution and low-quality images. To address this issue, we propose the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge. This challenge includes a large dataset of around ...
Source: IEE Transactions on Medical Imaging - September 15, 2023 Category: Biomedical Engineering Source Type: research

Federated Semi-Supervised Medical Image Segmentation via Prototype-Based Pseudo-Labeling and Contrastive Learning
Existing federated learning works mainly focus on the fully supervised training setting. In realistic scenarios, however, most clinical sites can only provide data without annotations due to the lack of resources or expertise. In this work, we are concerned with the practical yet challenging federated semi-supervised segmentation (FSSS), where labeled data are only with several clients and other clients can just provide unlabeled data. We take an early attempt to tackle this problem and propose a novel FSSS method with prototype-based pseudo-labeling and contrastive learning. First, we transmit a labeled-aggregated model, ...
Source: IEE Transactions on Medical Imaging - September 13, 2023 Category: Biomedical Engineering Source Type: research

Time-Lagged Functional Ultrasound for Multi-Parametric Cerebral Hemodynamic Imaging
We introduce an ultrasound speckle decorrelation-based time-lagged functional ultrasound technique (tl-fUS) for the quantification of the relative changes in cerebral blood flow speed (rCBF $_{\text {speed}}$ ), cerebral blood volume (rCBV) and cerebral blood flow (rCBF) during functional stimulations. Numerical simulations, phantom validations, and in vivo mouse brain experiments were performed to test the capability of tl-fUS to parse out and quantify the ratio change of these hemodynamic parameters. The blood volume change was found to be more prominent in arterioles compared to venules and the peak blood flow changes w...
Source: IEE Transactions on Medical Imaging - September 13, 2023 Category: Biomedical Engineering Source Type: research

ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer
The accelerating magnetic resonance imaging (MRI) reconstruction process is a challenging ill-posed inverse problem due to the excessive under-sampling operation in ${k}$ -space. In this paper, we propose a recurrent Transformer model, namely ReconFormer, for MRI reconstruction, which can iteratively reconstruct high-fidelity magnetic resonance images from highly under-sampled ${k}$ -space data (e.g., up to $8\times $ acceleration). In particular, the proposed architecture is built upon Recurrent Pyramid Transformer Layers (RPTLs). The core design of the proposed method is Recurrent Scale-wise Attention (RSA), which jointl...
Source: IEE Transactions on Medical Imaging - September 13, 2023 Category: Biomedical Engineering Source Type: research

Digital Staining of White Blood Cells With Confidence Estimation
Chemical staining of the blood smears is one of the crucial components of blood analysis. It is an expensive, lengthy and sensitive process, often prone to produce slight variations in colour and seen structures due to a lack of unified protocols across laboratories. Even though the current developments in deep generative modeling offer an opportunity to replace the chemical process with a digital one, there are specific safety-ensuring requirements due to the severe consequences of mistakes in a medical setting. Therefore digital staining system would profit from an additional confidence estimation quantifying the quality...
Source: IEE Transactions on Medical Imaging - September 12, 2023 Category: Biomedical Engineering Source Type: research

Deep Cascade-Learning Model via Recurrent Attention for Immunofixation Electrophoresis Image Analysis
Immunofixation Electrophoresis (IFE) analysis has been an indispensable prerequisite for the diagnosis of M-protein, which is an important criterion to recognize diversified plasma cell diseases. Existing intelligent methods of IFE diagnosis commonly employ a single unified classifier to directly classify whether M-protein exists and which isotype of M-protein is. However, this unified classification is not optimal because the two tasks have different characteristics and require different feature extraction techniques. Classifying the M-protein existence depends on the presence or absence of dense bands in IFE data, while ...
Source: IEE Transactions on Medical Imaging - September 12, 2023 Category: Biomedical Engineering Source Type: research

Self-Supervised Multi-Scale Cropping and Simple Masked Attentive Predicting for Lung CT-Scan Anomaly Detection
Anomaly detection has been widely explored by training an out-of-distribution detector with only normal data for medical images. However, detecting local and subtle irregularities without prior knowledge of anomaly types brings challenges for lung CT-scan image anomaly detection. In this paper, we propose a self-supervised framework for learning representations of lung CT-scan images via both multi-scale cropping and simple masked attentive predicting, which is capable of constructing a powerful out-of-distribution detector. Firstly, we propose CropMixPaste, a self-supervised augmentation task for generating density shadow...
Source: IEE Transactions on Medical Imaging - September 11, 2023 Category: Biomedical Engineering Source Type: research

: A Large-Scale Benchmark for Rib Labeling and Anatomical Centerline Extraction
Automatic rib labeling and anatomical centerline extraction are common prerequisites for various clinical applications. Prior studies either use in-house datasets that are inaccessible to communities, or focus on rib segmentation that neglects the clinical significance of rib labeling. To address these issues, we extend our prior dataset (RibSeg) on the binary rib segmentation task to a comprehensive benchmark, named RibSeg v2, with 660 CT scans (15,466 individual ribs in total) and annotations manually inspected by experts for rib labeling and anatomical centerline extraction. Based on the RibSeg v2, we develop a pipeline...
Source: IEE Transactions on Medical Imaging - September 11, 2023 Category: Biomedical Engineering Source Type: research

Joint Cross-Attention Network With Deep Modality Prior for Fast MRI Reconstruction
Current deep learning-based reconstruction models for accelerated multi-coil magnetic resonance imaging (MRI) mainly focus on subsampled k-space data of single modality using convolutional neural network (CNN). Although dual-domain information and data consistency constraint are commonly adopted in fast MRI reconstruction, the performance of existing models is still limited mainly by three factors: inaccurate estimation of coil sensitivity, inadequate utilization of structural prior, and inductive bias of CNN. To tackle these challenges, we propose an unrolling-based joint Cross-Attention Network, dubbed as jCAN, using dee...
Source: IEE Transactions on Medical Imaging - September 11, 2023 Category: Biomedical Engineering Source Type: research

SWSSL: Sliding Window-Based Self-Supervised Learning for Anomaly Detection in High-Resolution Images
Anomaly detection (AD) aims to determine if an instance has properties different from those seen in normal cases. The success of this technique depends on how well a neural network learns from normal instances. We observe that the learning difficulty scales exponentially with the input resolution, making it infeasible to apply AD to high-resolution images. Resizing them to a lower resolution is a compromising solution and does not align with clinical practice where the diagnosis could depend on image details. In this work, we propose to train the network and perform inference at the patch level, through the sliding window ...
Source: IEE Transactions on Medical Imaging - September 11, 2023 Category: Biomedical Engineering Source Type: research

Deep Semi-Supervised Ultrasound Image Segmentation by Using a Shadow Aware Network With Boundary Refinement
Accurate ultrasound (US) image segmentation is crucial for the screening and diagnosis of diseases. However, it faces two significant challenges: 1) pixel-level annotation is a time-consuming and laborious process; 2) the presence of shadow artifacts leads to missing anatomy and ambiguous boundaries, which negatively impact reliable segmentation results. To address these challenges, we propose a novel semi-supervised shadow aware network with boundary refinement (SABR-Net). Specifically, we add shadow imitation regions to the original US, and design shadow-masked transformer blocks to perceive missing anatomy of shadow reg...
Source: IEE Transactions on Medical Imaging - September 11, 2023 Category: Biomedical Engineering Source Type: research

Learning From Incorrectness: Active Learning With Negative Pre-Training and Curriculum Querying for Histological Tissue Classification
Patch-level histological tissue classification is an effective pre-processing method for histological slide analysis. However, the classification of tissue with deep learning requires expensive annotation costs. To alleviate the limitations of annotation budgets, the application of active learning (AL) to histological tissue classification is a promising solution. Nevertheless, there is a large imbalance in performance between categories during application, and the tissue corresponding to the categories with relatively insufficient performance are equally important for cancer diagnosis. In this paper, we propose an active ...
Source: IEE Transactions on Medical Imaging - September 8, 2023 Category: Biomedical Engineering Source Type: research

MG-Trans: Multi-Scale Graph Transformer With Information Bottleneck for Whole Slide Image Classification
Multiple instance learning (MIL)-based methods have become the mainstream for processing the megapixel-sized whole slide image (WSI) with pyramid structure in the field of digital pathology. The current MIL-based methods usually crop a large number of patches from WSI at the highest magnification, resulting in a lot of redundancy in the input and feature space. Moreover, the spatial relations between patches can not be sufficiently modeled, which may weaken the model’s discriminative ability on fine-grained features. To solve the above limitations, we propose a Multi-scale Graph Transformer (MG-Trans) with information bo...
Source: IEE Transactions on Medical Imaging - September 8, 2023 Category: Biomedical Engineering Source Type: research