A novel AI model for detecting periapical lesion on CBCT: CBCT-SAM.

Objectives: Periapical lesions are not always evident on radiographic scans. Sometimes, asymptomatic or initial periapical lesions on cone-beam computed tomography (CBCT) could be missed by inexperienced dentists, especially when the scan has a large field of view and is not for endodontic treatment purposes. Previously, numerous algorithms have been introduced to assist radiographic assessment and diagnosis in the field of endodontics.

Exosomes as novel biomarkers in sepsis and sepsis related organ failure.

Sepsis, a severe and life-threatening condition arising from a dysfunctional host response to infection, presents considerable challenges to the health care system and is characterized by high mortality rates and substantial economic costs. Exosomes have garnered attention as potential diagnostic markers because of their capacity to mirror the pathophysiological milieu of sepsis. This discourse reviews the progression of sepsis classification from Sepsis 1.

Brain functional gradients are related to cortical folding gradient.

Cortical folding is closely linked to brain functions, with gyri acting more like local functional "hubs" to integrate information than sulci do. However, understanding how anatomical constraints relate to complex functions remains fragmented. One possible reason is that the relationship is estimated on brain mosaics divided by brain functions and cortical folding patterns.

Exosome-Derived microRNA: Potential Target for Diagnosis and Treatment of Sepsis.

Exosome-derived microRNAs (miRNAs) are emerging as pivotal players in the pathophysiology of sepsis, representing a new frontier in both the diagnosis and treatment of this complex condition. Sepsis, a severe systemic response to infection, involves intricate immune and nonimmune mechanisms, where exosome-mediated communication can significantly influence disease progression and outcomes. During the progress of sepsis, the miRNA profile of exosomes undergoes notable alterations, is reflecting, and may affect the progression of the disease.

Global and multi-partition local network analysis of scalp EEG in West syndrome before and after treatment.

West syndrome is an epileptic disease that seriously affects the normal growth and development of infants in early childhood. Based on the methods of brain topological network and graph theory, this article focuses on three clinical states of patients before and after treatment. In addition to discussing bidirectional and unidirectional global networks from the perspective of computational principles, a more in-depth analysis of local intra-network and inter-network characteristics of multi-partitioned networks is also performed.

Dual domain distribution disruption with semantics preservation: Unsupervised domain adaptation for medical image segmentation.

Recent unsupervised domain adaptation (UDA) methods in medical image segmentation commonly utilize Generative Adversarial Networks (GANs) for domain translation. However, the translated images often exhibit a distribution deviation from the ideal due to the inherent instability of GANs, leading to challenges such as visual inconsistency and incorrect style, consequently causing the segmentation model to fall into the fixed wrong pattern. To address this problem, we propose a novel UDA framework known as Dual Domain Distribution Disruption with Semantics Preservation (DDSP).

STAR-RL: Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolution.

Pathology image are essential for accurately interpreting lesion cells in cytopathology screening, but acquiring high-resolution digital slides requires specialized equipment and long scanning times. Though super-resolution (SR) techniques can alleviate this problem, existing deep learning models recover pathology image in a black-box manner, which can lead to untruthful biological details and misdiagnosis. Additionally, current methods allocate the same computational resources to recover each pixel of pathology image, leading to the sub-optimal recovery issue due to the large variation of pathology image.

MCPL: Multi-modal Collaborative Prompt Learning for Medical Vision-Language Model.

Multi-modal prompt learning is a high-performance and cost-effective learning paradigm, which learns text as well as image prompts to tune pre-trained vision-language (V-L) models like CLIP for adapting multiple downstream tasks. However, recent methods typically treat text and image prompts as independent components without considering the dependency between prompts. Moreover, extending multi-modal prompt learning into the medical field poses challenges due to a significant gap between general- and medical-domain data.

Universal and extensible language-vision models for organ segmentation and tumor detection from abdominal computed tomography.

The advancement of artificial intelligence (AI) for organ segmentation and tumor detection is propelled by the growing availability of computed tomography (CT) datasets with detailed, per-voxel annotations. However, these AI models often struggle with flexibility for partially annotated datasets and extensibility for new classes due to limitations in the one-hot encoding, architectural design, and learning scheme. To overcome these limitations, we propose a universal, extensible framework enabling a single model, termed Universal Model, to deal with multiple public datasets and adapt to new classes (e.

Mask-aware transformer with structure invariant loss for CT translation.

Multi-phase enhanced computed tomography (MPECT) translation from plain CT can help doctors to detect the liver lesion and prevent patients from the allergy during MPECT examination. Existing CT translation methods directly learn an end-to-end mapping from plain CT to MPECT, ignoring the crucial clinical domain knowledge. As clinicians subtract the plain CT from MPECT images as subtraction image to highlight the contrast-enhanced regions and further to facilitate liver disease diagnosis in the clinical diagnosis, we aim to exploit this domain knowledge for automatic CT translation.

MHD-Net: Memory-Aware Hetero-Modal Distillation Network for Thymic Epithelial Tumor Typing With Missing Pathology Modality.

Fusing multi-modal radiology and pathology data with complementary information can improve the accuracy of tumor typing. However, collecting pathology data is difficult since it is high-cost and sometimes only obtainable after the surgery, which limits the application of multi-modal methods in diagnosis. To address this problem, we propose comprehensively learning multi-modal radiology-pathology data in training, and only using uni-modal radiology data in testing.

Integrated lithium niobate microwave photonic processing engine.

Integrated microwave photonics (MWP) is an intriguing technology for the generation, transmission and manipulation of microwave signals in chip-scale optical systems. In particular, ultrafast processing of analogue signals in the optical domain with high fidelity and low latency could enable a variety of applications such as MWP filters, microwave signal processing and image recognition. An ideal integrated MWP processing platform should have both an efficient and high-speed electro-optic modulation block to faithfully perform microwave-optic conversion at low power and also a low-loss functional photonic network to implement various signal-processing tasks.

Bioactive Polyurethane-Poly(ethylene Glycol) Diacrylate Hydrogels for Applications in Tissue Engineering.

Polyurethanes (PUs) are a highly adaptable class of biomaterials that are among some of the most researched materials for various biomedical applications. However, engineered tissue scaffolds composed of PU have not found their way into clinical application, mainly due to the difficulty of balancing the control of material properties with the desired cellular response. A simple method for the synthesis of tunable bioactive poly(ethylene glycol) diacrylate (PEGDA) hydrogels containing photocurable PU is described.

Causal Disentanglement Domain Generalization for time-series signal fault diagnosis.

Domain generalization-based fault diagnosis (DGFD) presents significant prospects for recognizing faults without the accessibility of the target domain. Previous DGFD methods have achieved significant progress; however, there are some limitations. First, most DGFG methods statistically model the dependence between time-series data and labels, and they are superficial descriptions to the actual data-generating process.

MGIML: Cancer Grading With Incomplete Radiology-Pathology Data via Memory Learning and Gradient Homogenization.

Taking advantage of multi-modal radiology-pathology data with complementary clinical information for cancer grading is helpful for doctors to improve diagnosis efficiency and accuracy. However, radiology and pathology data have distinct acquisition difficulties and costs, which leads to incomplete-modality data being common in applications. In this work, we propose a Memory- and Gradient-guided Incomplete Modal-modal Learning (MGIML) framework for cancer grading with incomplete radiology-pathology data.

Disentangle Then Calibrate With Gradient Guidance: A Unified Framework for Common and Rare Disease Diagnosis.

The computer-aided diagnosis (CAD) for rare diseases using medical imaging poses a significant challenge due to the requirement of large volumes of labeled training data, which is particularly difficult to collect for rare diseases. Although Few-shot learning (FSL) methods have been developed for this task, these methods focus solely on rare disease diagnosis, failing to preserve the performance in common disease diagnosis. To address this issue, we propose the Disentangle then Calibrate with Gradient Guidance (DCGG) framework under the setting of generalized few-shot learning, i.

Comprehensive learning and adaptive teaching: Distilling multi-modal knowledge for pathological glioma grading.

The fusion of multi-modal data, e.g., pathology slides and genomic profiles, can provide complementary information and benefit glioma grading.

Handling Open-Set Noise and Novel Target Recognition in Domain Adaptive Semantic Segmentation.

This paper studies a practical domain adaptive (DA) semantic segmentation problem where only pseudo-labeled target data is accessible through a black-box model. Due to the domain gap and label shift between two domains, pseudo-labeled target data contains mixed closed-set and open-set label noises. In this paper, we propose a simplex noise transition matrix (SimT) to model the mixed noise distributions in DA semantic segmentation, and leverage SimT to handle open-set label noise and enable novel target recognition.

Medical federated learning with joint graph purification for noisy label learning.

In terms of increasing privacy issues, Federated Learning (FL) has received extensive attention in medical imaging. Through collaborative training, FL can produce superior diagnostic models with global knowledge, while preserving private data locally. In practice, medical diagnosis suffers from intra-/inter-observer variability, thus label noise is inevitable in dataset preparation.

Dynamic feature splicing for few-shot rare disease diagnosis.

Annotated images for rare disease diagnosis are extremely hard to collect. Therefore, identifying rare diseases under a few-shot learning (FSL) setting is significant. Existing FSL methods transfer useful and global knowledge from base classes with abundant training samples to enrich features of novel classes with few training samples, but still face difficulties when being applied to medical images due to the complex lesion characteristics and large intra-class variance.

Self-Supervised Interactive Embedding for One-Shot Organ Segmentation.

One-shot organ segmentation (OS2) aims at segmenting the desired organ regions from the input medical imaging data with only one pre-annotated example as the reference. By using the minimal annotation data to facilitate organ segmentation, OS2 receives great attention in the medical image analysis community due to its weak requirement on human annotation. In OS2, one core issue is to explore the mutual information between the support (reference slice) and the query (test slice).

Disentangle First, Then Distill: A Unified Framework for Missing Modality Imputation and Alzheimer's Disease Diagnosis.

Multi-modality medical data provide complementary information, and hence have been widely explored for computer-aided AD diagnosis. However, the research is hindered by the unavoidable missing-data problem, i.e.

FedOSS: Federated Open Set Recognition via Inter-Client Discrepancy and Collaboration.

Open set recognition (OSR) aims to accurately classify known diseases and recognize unseen diseases as the unknown class in medical scenarios. However, in existing OSR approaches, gathering data from distributed sites to construct large-scale centralized training datasets usually leads to high privacy and security risk, which could be alleviated elegantly via the popular cross-site training paradigm, federated learning (FL). To this end, we represent the first effort to formulate federated open set recognition (FedOSR), and meanwhile propose a novel Federated Open Set Synthesis (FedOSS) framework to address the core challenge of FedOSR: the unavailability of unknown samples for all anticipated clients during the training phase.