Latest AI and machine learning research in peptic ulcer disease for healthcare professionals.
Accurate depth estimation plays a critical role in the navigation of endoscopic surgical robots, forming the foundation for 3D reconstruction and safe instrument guidance. Fine-tuning pretrained models heavily relies on endoscopic surgical datasets with precise depth annotations. While existing self-supervised depth estimation techniques eliminate the need for accurate depth annotations, their per...
Triple-negative breast cancer (TNBC) is an aggressive subtype that lacks effective targeted therapies. This study aimed to identify robust prognostic biomarkers by integrating network biology with machine learning (ML) approaches. TNBC expression cohorts were analysed to identify differentially expressed genes (DEGs) and crucial gene clusters via limma and Weighted Gene Co-expression Network Analy...
Endoscopy is essential in medical imaging, used for diagnosis, prognosis and treatment. Developing a robust dynamic 3D reconstruction pipeline for end...
Objective: The immune system plays a role in the occurrence and progression of numerous pregnancy complications, particularly preeclampsia (PE). This ...
Urinary bladder cancer surveillance requires tracking tumor sites across repeated interventions, yet the deformable and hollow bladder lacks stable la...
The classification performance of deep neural networks relies strongly on access to large, accurately annotated datasets. In medical imaging, however,...
A priori model informed precision dosing (MIPD) recommends an appropriate first dose based solely on the covariates of the patient enabling faster tar...
Early detection of colorectal cancer hinges on real-time, accurate polyp identification and resection. Yet current high-precision segmentation models ...
We study the problem of monitoring model performance in dynamic environments where labeled data are limited. To this end, we propose prediction-powere...
Protein design seeks optimal amino acid sequences for target structures, but designing stable protein complexes remains challenging. We introduce a pr...
Machine learning predictions are increasingly used to supplement incomplete or costly-to-measure outcomes in fields such as biomedical research, envir...
Protein-protein interactions (PPIs) are governed by two fundamental interfacial mechanisms: similarity-driven, often involving symmetric structural mo...
Protein-protein interactions (PPIs) and nanobody-antigen interactions (NAIs) play essential roles in cellular function, yet accurate hotspot predictio...
Identifying unique polyps in colon capsule endoscopy (CCE) images is a critical yet challenging task for medical personnel due to the large volume of ...
Artificial intelligence has reshaped medical imaging, yet the use of AI on clinical data for prospective decision support remains limited. We study pr...
Detecting anatomical landmarks in medical imaging is essential for diagnosis and intervention guidance. However, object detection models rely on costl...
Image Dehazing (ID) aims to produce a clear image from an observation contaminated by haze. Current ID methods typically rely on carefully crafted pri...
In this study, we explore the application of deep learning techniques for predicting cleansing quality in colon capsule endoscopy (CCE) images. Using ...
Subsurface scattering (SSS) gives translucent materials -- such as wax, jade, marble, and skin -- their characteristic soft shadows, color bleeding, a...
Traditional surgical training relies heavily on hands-on experiences gained through relatively infrequent procedures during apprenticeships. Recently,...