Latest AI and machine learning research in alternative medicine for healthcare professionals.
Multimodal Fusion Learning (MFL), leveraging disparate data from various imaging modalities (e.g., MRI, CT, SPECT), has shown great potential for addressing medical problems such as skin cancer and brain tumor prediction. However, existing MFL methods face three key limitations: a) they often specialize in specific modalities, and overlook effective shared complementary information across diverse ...
Precision medicine requires models that can translate rich molecular measurements into individualized predictions of biological response. Phosphoinositide signaling disorders present an acute challenge, where nonlinear dynamics vary across cell types and are difficult to predict or interpret from measurements alone without mechanistic modeling. We developed a sensitivity analysis-guided New Approa...
Deep learning has achieved expert-level performance in automated electrocardiogram (ECG) diagnosis, yet the "black-box" nature of these models hinders...
Histone modifications underpin the cell-type-specific gene regulatory networks that drive the remarkable cellular heterogeneity of the adult mammalian...
Existing cross-modal pedestrian detection (CMPD) employs complementary information from RGB and thermal-infrared (TIR) modalities to detect pedestrian...
This study explores the integration of multiple Explainable AI (XAI) techniques to enhance the interpretability of deep learning models for brain tumo...
Machine learning (ML) in medicine has transitioned from research to concrete applications aimed at supporting several medical purposes like therapy se...
Multimodal learning aims to integrate complementary information from heterogeneous modalities, yet strong optimization alone does not guaranty well-st...
Accurate prediction of drug response in precision medicine requires models that capture how specific chemical substructures interact with cellular pat...
Zero-shot composed image retrieval (ZS-CIR) is a rapidly growing area with significant practical applications, allowing users to retrieve a target ima...
In this study, we explore the application of deep learning techniques for predicting cleansing quality in colon capsule endoscopy (CCE) images. Using ...
Deep Research Agents (DRAs) generate citation-rich reports via multi-step search and synthesis, yet existing benchmarks mainly target text-only settin...
Background: Pneumonia remains a leading cause of morbidity and mortality among children worldwide, emphasizing the need for accurate and efficient dia...
The shortage in early detection methods for the pathogen Burkholderia gladioli pv. cocovenenans (BGC) and its toxin bongkrekic acid rises the risk for...
Animal growth is driven by the collective actions of cells, which are reciprocally influenced in real-time by the animal's overall growth state. Where...
Models like OpenAI-o3 pioneer visual grounded reasoning by dynamically referencing visual regions, just like human "thinking with images". However, ...
Deep neural networks suffer from significant performance degradation when exposed to common corruptions such as noise, blur, weather, and digital di...
Colorectal cancer (CRC) is closely linked to the malignant transformation of colorectal polyps, making early detection essential. However, current m...
Cardiac rehabilitation is a crucial multidisciplinary approach to improve patient outcomes. There is a growing body of evidence that suggests that the...
While image dehazing has advanced substantially in the past decade, most efforts have focused on short-range scenarios, leaving long-range haze remo...