Latest AI and machine learning research in colon cancer for healthcare professionals.
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by profound molecular heterogeneity and inconsistent responses to gemcitabine-based therapy. Although KRAS mutations are nearly ubiquitous, the broader RTK-RAS and MAPK signaling networks, and their association with therapeutic response, remain insufficiently characterized. We performed an integrative clinical-genomi...
Differences in microbiome composition profoundly influence drug response, yet methods to model the metabolic interplay between tumors, microbes, and therapeutics remain limited. We present a generalizable framework combining machine learning and genome scale metabolic modeling to prioritize combination therapies for colorectal cancer (CRC) in the presence of Fusobacterium nucleatum (Fn) and other ...
Video polyp segmentation (VPS) is an important task in computer-aided colonoscopy, as it helps doctors accurately locate and track polyps during exami...
Colonoscopy video generation delivers dynamic, information-rich data critical for diagnosing intestinal diseases, particularly in data-scarce scenario...
Monocular depth estimation (MDE) for colonoscopy is hampered by the domain gap between simulated and real-world images. Existing image-to-image transl...
Cancer driver mutations shape the tumor microenvironment (TME), yet whether TME composition alone can predict genotype has not been systematically eva...
We introduce a workflow that integrates BioEmu-generated conformational ensemble with physics-based molecular simulations and Markov State Models to s...
Radiomics and deep learning both offer powerful tools for quantitative medical imaging, but most existing fusion approaches only leverage global radio...
Monocular depth and pose estimation play an important role in the development of colonoscopy-assisted navigation, as they enable improved screening by...
Accurate grasping point prediction is a key challenge for autonomous tissue manipulation in minimally invasive surgery, particularly in complex and va...
Background: Pancreatic ductal adenocarcinoma is one of the most aggressive and lethal malignancies of the gastrointestinal tract. The poor prognosis i...
Pancreatic ductal adenocarcinoma remains one of the most lethal malignancies, largely due to the absence of reliable early stage biomarkers. Here, we ...
Colorectal cancer (CRC) remains a significant cause of cancer-related mortality, despite the widespread implementation of prophylactic initiatives aim...
Deep learning has the potential to improve colonoscopy by enabling 3D reconstruction of the colon, providing a comprehensive view of mucosal surfaces ...
Abstract Background: Colorectal cancer (CRC) is a leading cause of cancer mortality. While early detection improves outcomes, current non-invasive tes...
Purpose: To develop SCOPE (Small-lesion COntextual Pancreatic Evaluator), a deep learning model designed to improve CT detection of small pancreatic l...
This study investigates the efficacy of transformer-based deep learning architectures-specifically, Vision Transformer (ViT), Class Attention in Image...
Digitizing large histopathology archives requires processing millions of scanned whole slide images that must be validated rapidly. Automated organ-of...
Early detection of colorectal cancer hinges on real-time, accurate polyp identification and resection. Yet current high-precision segmentation models ...
Accurate risk stratification of precancerous polyps during routine colonoscopy screenings is essential for lowering the risk of developing colorectal ...