Chest X-Ray (CXR) imaging for pulmonary diagnosis raises significant challenges, primarily because bone structures can obscure critical details necessary for accurate diagnosis. Recent advances in deep learning, particularly with diffusion models, offer significant promise for effectively minimizing the visibility of bone structures in CXR images, thereby improving clarity and diagnostic accuracy. Nevertheless, existing diffusion-based methods for bone suppression in CXR imaging struggle to balance the complete suppression of bones with preserving local texture details. Additionally, their high computational demand and extended processing time hinder their practical use in clinical settings. To address these limitations, we introduce a Global-Local Latent Consistency Model (GL-LCM) architecture. This model combines lung segmentation, dual-path sampling, and global-local fusion, enabling fast high-resolution bone suppression in CXR images. To tackle potential boundary artifacts and detail blurring in local-path sampling, we further propose Local-Enhanced Guidance, which addresses these issues without additional training. Comprehensive experiments on a self-collected dataset SZCH-X-Rays, and the public dataset JSRT, reveal that our GL-LCM delivers superior bone suppression and remarkable computational efficiency, significantly outperforming several competitive methods. Our code is available at https://github.com/diaoquesang/GL-LCM.

Continual learning is essential for medical image classification systems to adapt to dynamically evolving clinical environments. The integration of multimodal information can significantly enhance continual learning of image classes. However, while existing approaches do utilize textual modality information, they solely rely on simplistic templates with a class name, thereby neglecting richer semantic information. To address these limitations, we propose a novel framework that harnesses visual concepts generated by large language models (LLMs) as discriminative semantic guidance. Our method dynamically constructs a visual concept pool with a similarity-based filtering mechanism to prevent redundancy. Then, to integrate the concepts into the continual learning process, we employ a cross-modal image-concept attention module, coupled with an attention loss. Through attention, the module can leverage the semantic knowledge from relevant visual concepts and produce class-representative fused features for classification. Experiments on medical and natural image datasets show our method achieves state-of-the-art performance, demonstrating the effectiveness and superiority of our method. We will release the code publicly.

Alzheimers Disease (AD) is a progressive neurodegenerative disorder that poses significant challenges in its early diagnosis, often leading to delayed treatment and poorer outcomes for patients. Traditional diagnostic methods, typically reliant on single data modalities, fall short of capturing the multifaceted nature of the disease. In this paper, we propose a novel multimodal framework for the early detection of AD that integrates data from three primary sources: MRI imaging, cognitive assessments, and biomarkers. This framework employs Convolutional Neural Networks (CNN) for analyzing MRI images and Long Short-Term Memory (LSTM) networks for processing cognitive and biomarker data. The system enhances diagnostic accuracy and reliability by aggregating results from these distinct modalities using advanced techniques like weighted averaging, even in incomplete data. The multimodal approach not only improves the robustness of the detection process but also enables the identification of AD at its earliest stages, offering a significant advantage over conventional methods. The integration of biomarkers and cognitive tests is particularly crucial, as these can detect Alzheimer's long before the onset of clinical symptoms, thereby facilitating earlier intervention and potentially altering the course of the disease. This research demonstrates that the proposed framework has the potential to revolutionize the early detection of AD, paving the way for more timely and effective treatments


Computed tomography perfusion (CTP) imaging is a rapid diagnostic tool for acute stroke but is less robust when tissue time-attenuation curves are truncated. This study proposes an unsupervised learning method for generating perfusion maps from truncated CTP images. Real brain CTP images were artificially truncated to 15% and 30% of the original scan time. Perfusion maps of complete and truncated CTP images were calculated using the proposed method and compared with standard singular value decomposition (SVD), tensor total variation (TTV), nonlinear regression (NLR), and spatio-temporal perfusion physics-informed neural network (SPPINN).
Main results.
The NLR method yielded many perfusion values outside physiological ranges, indicating a lack of robustness. The proposed method did not improve the estimation of cerebral blood flow compared to both the SVD and TTV methods, but reduced the effect of truncation on the estimation of cerebral blood volume, with a relative difference of 15.4% in the infarcted region for 30% truncation (20.7% for SVD and 19.4% for TTV). The proposed method also showed better resistance to 30% truncation for mean transit time, with a relative difference of 16.6% in the infarcted region (25.9% for SVD and 26.2% for TTV). Compared to the SPPINN method, the proposed method had similar responses to truncation in gray and white matter, but was less sensitive to truncation in the infarcted region. These results demonstrate the feasibility of using unsupervised learning to generate perfusion maps from CTP images and improve robustness under truncation scenarios.&#xD.

Radiologists often mix medical image reading strategies, including inspection of individual modalities and local image regions, using information at different locations from different images independently as well as concurrently. In this paper, we propose a recommend system to assist machine learning-based segmentation models, by suggesting appropriate image portions along with the best modality, such that prostate cancer segmentation performance can be maximised. Our approach trains a policy network that assists tumor localisation, by recommending both the optimal imaging modality and the specific sections of interest for review. During training, a pre-trained segmentation network mimics radiologist inspection on individual or variable combinations of these imaging modalities and their sections - selected by the policy network. Taking the locally segmented regions as an input for the next step, this dynamic decision making process iterates until all cancers are best localised. We validate our method using a data set of 1325 labelled multiparametric MRI images from prostate cancer patients, demonstrating its potential to improve annotation efficiency and segmentation accuracy, especially when challenging pathology is present. Experimental results show that our approach can surpass standard segmentation networks. Perhaps more interestingly, our trained agent independently developed its own optimal strategy, which may or may not be consistent with current radiologist guidelines such as PI-RADS. This observation also suggests a promising interactive application, in which the proposed policy networks assist human radiologists.

We propose a diffusion model designed to generate point-based shape representations with correspondences. Traditional statistical shape models have considered point correspondences extensively, but current deep learning methods do not take them into account, focusing on unordered point clouds instead. Current deep generative models for point clouds do not address generating shapes with point correspondences between generated shapes. This work aims to formulate a diffusion model that is capable of generating realistic point-based shape representations, which preserve point correspondences that are present in the training data. Using shape representation data with correspondences derived from Open Access Series of Imaging Studies 3 (OASIS-3), we demonstrate that our correspondence-preserving model effectively generates point-based hippocampal shape representations that are highly realistic compared to existing methods. We further demonstrate the applications of our generative model by downstream tasks, such as conditional generation of healthy and AD subjects and predicting morphological changes of disease progression by counterfactual generation.

Parkinson's disease (PD) presents challenges in early diagnosis and progression prediction. Recent advancements in machine learning, particularly convolutional-neural-networks (CNNs), show promise in enhancing diagnostic accuracy and prognostic capabilities using neuroimaging data. The aims of this study were: (i) develop a 3D-CNN based on MRI to distinguish controls and PD patients and (ii) employ CNN to predict the progression of PD. Three cohorts were selected: 86 mild, 62 moderate-to-severe PD patients, and 60 controls; 14 mild-PD patients and 14 controls from Parkinson's Progression Markers Initiative database, and 38 de novo mild-PD patients and 38 controls. All participants underwent MRI scans and clinical evaluation at baseline and over 2-years. PD subjects were classified in two clusters of different progression using k-means clustering based on baseline and follow-up UDPRS-III scores. A 3D-CNN was built and tested on PD patients and controls, with binary classifications: controls vs moderate-to-severe PD, controls vs mild-PD, and two clusters of PD progression. The effect of transfer learning was also tested. CNN effectively differentiated moderate-to-severe PD from controls (74% accuracy) using MRI data alone. Transfer learning significantly improved performance in distinguishing mild-PD from controls (64% accuracy). For predicting disease progression, the model achieved over 70% accuracy by combining MRI and clinical data. Brain regions most influential in the CNN's decisions were visualized. CNN, integrating multimodal data and transfer learning, provides encouraging results toward early-stage classification and progression monitoring in PD. Its explainability through activation maps offers potential for clinical application in early diagnosis and personalized monitoring.

The development of efficient segmentation strategies for medical images has evolved from its initial dependence on Convolutional Neural Networks (CNNs) to the current investigation of hybrid models that combine CNNs with Vision Transformers (ViTs). There is an increasing focus on developing architectures that are both high-performing and computationally efficient, capable of being deployed on remote systems with limited resources. Although transformers can capture global dependencies in the input space, they face challenges from the corresponding high computational and storage expenses involved. The objective of this research is to propose that Vision Extended Long Short-Term Memory (Vision-xLSTM) forms an appropriate backbone for medical image segmentation, offering excellent performance with reduced computational costs. This study investigates the integration of CNNs with Vision-xLSTM by introducing the novel U-VixLSTM. The Vision-xLSTM blocks capture the temporal and global relationships within the patches extracted from the CNN feature maps. The convolutional feature reconstruction path upsamples the output volume from the Vision-xLSTM blocks to produce the segmentation output. The U-VixLSTM exhibits superior performance compared to the state-of-the-art networks in the publicly available Synapse, ISIC and ACDC datasets. The findings suggest that U-VixLSTM is a promising alternative to ViTs for medical image segmentation, delivering effective performance without substantial computational burden. This makes it feasible for deployment in healthcare environments with limited resources for faster diagnosis. Code provided: https://github.com/duttapallabi2907/U-VixLSTM.

Medical segmentation, a prominent task in medical image analysis utilizing artificial intelligence, plays a crucial role in computer-aided diagnosis and depends heavily on the quality of the training data. However, the availability of sufficient data is constrained by strict privacy regulations associated with medical data. To mitigate this issue, research on data augmentation has gained significant attention. Medical segmentation tasks require paired datasets consisting of medical images and annotation images, also known as mask images, which represent lesion areas or radiological information within the medical images. Consequently, it is essential to apply data augmentation to both image types. This study proposes a Controllable Mask Diffusion Model, a novel approach capable of controlling and generating new masks. This model leverages the binary structure of the mask to extract semantic information, namely, the mask's size, location, and count, which is then applied as multi-conditional input to a diffusion model via a regressor. Through the regressor, newly generated masks conform to the input semantic information, thereby enabling input-driven controllable generation. Additionally, a technique that analyzes correlation within semantic information was devised for large-scale data synthesis. The generative capacity of the proposed model was evaluated against real datasets, and the model's ability to control and generate new masks based on previously unseen semantic information was confirmed. Furthermore, the practical applicability of the model was demonstrated by augmenting the data with the generated data, applying it to segmentation tasks, and comparing the performance with and without augmentation. Additionally, experiments were conducted on single-label and multi-label masks, yielding superior results for both types. This demonstrates the potential applicability of this study to various areas within the medical field.

The construction of growth charts trained to predict age or developmental deviation (the brain-age index) based on structural/functional properties of the brain may be informative of childrens neurodevelopmental trajectories. When applied to both typically and atypically developing populations, results may indicate that a particular condition is associated with atypical maturation of certain brain networks. Here, we focus on the relationship between reading disorder (RD) and maturation of functional connectivity (FC) patterns in the prototypical reading/language network using a cross-sectional sample of N = 742 participants aged 6-21 years. A support vector regression model is trained to predict chronological age from FC data derived from a whole-brain model as well as multiple reduced models, which are trained on FC data generated from a successively smaller number of regions in the brains reading network. We hypothesized that the trained models would show systematic underestimation of brain network maturity for poor readers, particularly for the models trained with reading/language regions. Comparisons of the different models predictions revealed that while the whole-brain model outperforms the others in terms of overall prediction accuracy, all models successfully predicted brain maturity, including the one trained with the smallest amount of FC data. In addition, all models showed that reading ability affected the brain-age gap, with poor readers ages being underestimated and advanced readers ages being overestimated. Exploratory results demonstrated that the most important regions and connections for prediction were derived from the default mode and frontoparietal control networks. GlossaryDevelopmental dyslexia / reading disorder (RD): A specific learning disorder affecting reading ability in the absence of any other explanatory condition such as intellectual disability or visual impairment Support vector regression (SVR): A supervised machine learning technique which predicts continuous outcomes (such as chronological age) rather than classifying each observation; finds the best-fit function within a defined error margin Principal component analysis (PCA): A dimensionality reduction technique that transforms a high-dimensional dataset with many features per observation into a reduced set of principal components for each observation; each component is a linear combination of several original (correlated) features, and the final set of components are all orthogonal (uncorrelated) to one another Brain-age index: A numerical index quantifying deviation from the brains typical developmental trajectory for a single individual; may be based on a variety of morphometric or functional properties of the brain, resulting in different estimates for the same participant depending on the imaging modality used Brain-age gap (BAG): The difference, given in units of time, between a participants true chronological age and a predictive models estimated age for that participant based on brain data (Actual - Predicted); may be used as a brain-age index HighlightsO_LIA machine learning model trained on functional data predicted participants ages C_LIO_LIThe model showed variability in age prediction accuracy based on reading skills C_LIO_LIThe model highly weighted data from frontoparietal and default mode regions C_LIO_LINeural markers of reading and language are diffusely represented in the brain C_LI