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.

Semi-Supervised Medical Image Segmentation Using Cross-Style Consistency With Shape-Aware and Local Context Constraints
Despite the remarkable progress in semi-supervised medical image segmentation methods based on deep learning, their application to real-life clinical scenarios still faces considerable challenges. For example, insufficient labeled data often makes it difficult for networks to capture the complexity and variability of the anatomical regions to be segmented. To address these problems, we design a new semi-supervised segmentation framework that aspires to produce anatomically plausible predictions. Our framework comprises two parallel networks: shape-agnostic and shape-aware networks. These networks learn from each other, ena...
Source: IEE Transactions on Medical Imaging - November 30, 2023 Category: Biomedical Engineering Source Type: research

SWENet: A Physics-Informed Deep Neural Network (PINN) for Shear Wave Elastography
Shear wave elastography (SWE) enables the measurement of elastic properties of soft materials in a non-invasive manner and finds broad applications in various disciplines. The state-of-the-art SWE methods rely on the measurement of local shear wave speeds to infer material parameters and suffer from wave diffraction when applied to soft materials with strong heterogeneity. In the present study, we overcome this challenge by proposing a physics-informed neural network (PINN)-based SWE (SWENet) method. The spatial variation of elastic properties of inhomogeneous materials has been introduced in the governing equations, which...
Source: IEE Transactions on Medical Imaging - November 30, 2023 Category: Biomedical Engineering Source Type: research

X-Ray Dark-Field Signal Reduction Due to Hardening of the Visibility Spectrum
We present a theoretical model for this group of effects and verify it by comparison to the measurements. These findings have significant implications for the interpretation of dark-field signal strength in polychromatic measurements. (Source: IEE Transactions on Medical Imaging)
Source: IEE Transactions on Medical Imaging - November 30, 2023 Category: Biomedical Engineering Source Type: research

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Source: IEE Transactions on Medical Imaging - November 30, 2023 Category: Biomedical Engineering Source Type: research

A Learnable Counter-Condition Analysis Framework for Functional Connectivity-Based Neurological Disorder Diagnosis
In this study, we propose a novel unified framework that systemically integrates diagnoses (i.e., feature selection and feature extraction) and explanations. Notably, we devised an adaptive attention network as a feature selection approach to identify individual-specific disease-related connections. We also propose a functional network relational encoder that summarizes the global topological properties of FC by learning the inter-network relations without pre-defined edges between functional networks. Last but not least, our framework provides a novel explanatory power for neuroscientific interpretation, also termed count...
Source: IEE Transactions on Medical Imaging - November 29, 2023 Category: Biomedical Engineering Source Type: research

A Graph-Based Multi-Scale Approach With Knowledge Distillation for WSI Classification
The usage of Multi Instance Learning (MIL) for classifying Whole Slide Images (WSIs) has recently increased. Due to their gigapixel size, the pixel-level annotation of such data is extremely expensive and time-consuming, practically unfeasible. For this reason, multiple automatic approaches have been raised in the last years to support clinical practice and diagnosis. Unfortunately, most state-of-the-art proposals apply attention mechanisms without considering the spatial instance correlation and usually work on a single-scale resolution. To leverage the full potential of pyramidal structured WSI, we propose a graph-based ...
Source: IEE Transactions on Medical Imaging - November 28, 2023 Category: Biomedical Engineering Source Type: research

EAG-RS: A Novel Explainability-Guided ROI-Selection Framework for ASD Diagnosis via Inter-Regional Relation Learning
Deep learning models based on resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used to diagnose brain diseases, particularly autism spectrum disorder (ASD). Existing studies have leveraged the functional connectivity (FC) of rs-fMRI, achieving notable classification performance. However, they have significant limitations, including the lack of adequate information while using linear low-order FC as inputs to the model, not considering individual characteristics (i.e., different symptoms or varying stages of severity) among patients with ASD, and the non-explainability of the decision process. ...
Source: IEE Transactions on Medical Imaging - November 28, 2023 Category: Biomedical Engineering Source Type: research

LSKANet: Long Strip Kernel Attention Network for Robotic Surgical Scene Segmentation
Surgical scene segmentation is a critical task in Robotic-assisted surgery. However, the complexity of the surgical scene, which mainly includes local feature similarity (e.g., between different anatomical tissues), intraoperative complex artifacts, and indistinguishable boundaries, poses significant challenges to accurate segmentation. To tackle these problems, we propose the Long Strip Kernel Attention network (LSKANet), including two well-designed modules named Dual-block Large Kernel Attention module (DLKA) and Multiscale Affinity Feature Fusion module (MAFF), which can implement precise segmentation of surgical images...
Source: IEE Transactions on Medical Imaging - November 28, 2023 Category: Biomedical Engineering Source Type: research

Three Dimensional Microwave Data Inversion in Feature Space for Stroke Imaging
Microwave imaging is a promising method for early diagnosing and monitoring brain strokes. It is portable, non-invasive, and safe to the human body. Conventional techniques solve for unknown electrical properties represented as pixels or voxels, but often result in inadequate structural information and high computational costs. We propose to reconstruct the three dimensional (3D) electrical properties of the human brain in a feature space, where the unknowns are latent codes of a variational autoencoder (VAE). The decoder of the VAE, with prior knowledge of the brain, acts as a module of data inversion. The codes in the fe...
Source: IEE Transactions on Medical Imaging - November 28, 2023 Category: Biomedical Engineering Source Type: research

SGT++: Improved Scene Graph-Guided Transformer for Surgical Report Generation
Automatically recording surgical procedures and generating surgical reports are crucial for alleviating surgeons’ workload and enabling them to concentrate more on the operations. Despite some achievements, there still exist several issues for the previous works: 1) failure to model the interactive relationship between surgical instruments and tissue; and 2) neglect of fine-grained differences within different surgical images in the same surgery. To address these two issues, we propose an improved scene graph-guided Transformer, also named by SGT++, to generate more accurate surgical report, in which the complex interact...
Source: IEE Transactions on Medical Imaging - November 28, 2023 Category: Biomedical Engineering Source Type: research

Enhancing and Adapting in the Clinic: Source-Free Unsupervised Domain Adaptation for Medical Image Enhancement
Medical imaging provides many valuable clues involving anatomical structure and pathological characteristics. However, image degradation is a common issue in clinical practice, which can adversely impact the observation and diagnosis by physicians and algorithms. Although extensive enhancement models have been developed, these models require a well pre-training before deployment, while failing to take advantage of the potential value of inference data after deployment. In this paper, we raise an algorithm for source-free unsupervised domain adaptive medical image enhancement (SAME), which adapts and optimizes enhancement m...
Source: IEE Transactions on Medical Imaging - November 28, 2023 Category: Biomedical Engineering Source Type: research

Efficient Supervised Pretraining of Swin-Transformer for Virtual Staining of Microscopy Images
Fluorescence staining is an important technique in life science for labeling cellular constituents. However, it also suffers from being time-consuming, having difficulty in simultaneous labeling, etc. Thus, virtual staining, which does not rely on chemical labeling, has been introduced. Recently, deep learning models such as transformers have been applied to virtual staining tasks. However, their performance relies on large-scale pretraining, hindering their development in the field. To reduce the reliance on large amounts of computation and data, we construct a Swin-transformer model and propose an efficient supervised pr...
Source: IEE Transactions on Medical Imaging - November 27, 2023 Category: Biomedical Engineering Source Type: research

SC-SSL: Self-Correcting Collaborative and Contrastive Co-Training Model for Semi-Supervised Medical Image Segmentation
Image segmentation achieves significant improvements with deep neural networks at the premise of a large scale of labeled training data, which is laborious to assure in medical image tasks. Recently, semi-supervised learning (SSL) has shown great potential in medical image segmentation. However, the influence of the learning target quality for unlabeled data is usually neglected in these SSL methods. Therefore, this study proposes a novel self-correcting co-training scheme to learn a better target that is more similar to ground-truth labels from collaborative network outputs. Our work has three-fold highlights. First, we a...
Source: IEE Transactions on Medical Imaging - November 23, 2023 Category: Biomedical Engineering Source Type: research

Adversarial Medical Image With Hierarchical Feature Hiding
Deep learning based methods for medical images can be easily compromised by adversarial examples (AEs), posing a great security flaw in clinical decision-making. It has been discovered that conventional adversarial attacks like PGD which optimize the classification logits, are easy to distinguish in the feature space, resulting in accurate reactive defenses. To better understand this phenomenon and reassess the reliability of the reactive defenses for medical AEs, we thoroughly investigate the characteristic of conventional medical AEs. Specifically, we first theoretically prove that conventional adversarial attacks change...
Source: IEE Transactions on Medical Imaging - November 23, 2023 Category: Biomedical Engineering Source Type: research

Latent Graph Representations for Critical View of Safety Assessment
Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality of their exposure. Prior works have approached this task by including semantic segmentation as an intermediate step, using predicted segmentation masks to then predict the CVS. While these methods are effective, they rely on extremely expensive ground-truth segmentation annotations and tend to fail when the predicted segmentation is incorrect, limiting generalization. In this work, we pr...
Source: IEE Transactions on Medical Imaging - November 16, 2023 Category: Biomedical Engineering Source Type: research