Colon Cancer

Transcriptomics-based modeling of methionine metabolism effectively estimates sample-wise DNA methylation activity and epigenetic aging

DNA methylation is a central epigenetic modification that regulates gene expression, maintains genomic stability, and guides cellular differentiation. However, direct measurements of DNA methylation, such as whole genome bisulfite sequencing or DNA methylation arrays, are costly and require substantial DNA input, limiting their scalability for large cohorts and their applicability to emerging moda...

A multimodal AI biomarker PATH-ORACLE improves prediction of recurrence in stage I lung adenocarcinoma

The standard treatment for stage I lung adenocarcinoma is surgical resection, in most cases without additional systemic adjuvant treatment. A significant proportion of stage I cases recur with a less than 50% 5-year survival rate. There are clinical data suggesting that adjuvant treatment may improve survival in such recurrent cases. However, previously evaluated predictors such as the IASLC gradi...

EndoCaver: Handling Fog, Blur and Glare in Endoscopic Images via Joint Deblurring-Segmentation

Endoscopic image analysis is vital for colorectal cancer screening, yet real-world conditions often suffer from lens fogging, motion blur, and specula...

A neural network model delivers a highly prognostic protein signature in cancer stem cells that identifies relapse in stage III colorectal cancer patients.

Background Stage III colorectal cancer poses a significant threat of metastasis development, as tumour resection and adjuvant chemotherapy do not guar...

Early and Prediagnostic Detection of Pancreatic Cancer from Computed Tomography

Pancreatic ductal adenocarcinoma (PDAC), one of the deadliest solid malignancies, is often detected at a late and inoperable stage. Retrospective revi...

Evaluating Spiking and Non-Spiking Neural Networks for Colorectal Serrated Polyp Subtype Classification

Image classification on digital pathology images relies heavily on convolutional neural networks (CNNs), yet the behavior of alternative neural comput...

From Specialist to Generalist: Unlocking SAM's Learning Potential on Unlabeled Medical Images

Foundation models like the Segment Anything Model (SAM) show strong generalization, yet adapting them to medical images remains difficult due to domai...

Predicting Protein Cascade Expression from H&E Images

Protein expression within oncogenic or suppressive pathways is a hallmark indicator of oncogenesis. While traditional AI models in digital pathology a...

Learning with Geometric Priors in U-Net Variants for Polyp Segmentation

Accurate and robust polyp segmentation is essential for early colorectal cancer detection and for computer-aided diagnosis. While convolutional neural...

Predicting Gene Mutations in Colon Cancer Using Long-Term Temporal Dependency Learning on a Directed Co-Occurrence Asymmetry Graph

Accurate prediction of mutational dependencies to model tumor evolution can improve our understanding of cancer progression and is crucial for early d...

Semi-Supervised Domain Adaptation with Latent Diffusion for Pathology Image Classification

Deep learning models in computational pathology often fail to generalize across cohorts and institutions due to domain shift. Existing approaches eith...

Phi-SegNet: Phase-Integrated Supervision for Medical Image Segmentation

Deep learning has substantially advanced medical image segmentation, yet achieving robust generalization across diverse imaging modalities and anatomi...

Transfer Learning from One Cancer to Another via Deep Learning Domain Adaptation

Supervised deep learning models often achieve excellent performance within their training distribution but struggle to generalize beyond it. In cancer...

Using Multi-Instance Learning to Identify Unique Polyps in Colon Capsule Endoscopy Images

Identifying unique polyps in colon capsule endoscopy (CCE) images is a critical yet challenging task for medical personnel due to the large volume of ...

Who Should Have Surgery? A Comparative Study of GenAI vs Supervised ML for CRS Surgical Outcome Prediction

Artificial intelligence has reshaped medical imaging, yet the use of AI on clinical data for prospective decision support remains limited. We study pr...

Generalizable Hyperparameter Optimization for Federated Learning on Non-IID Cancer Images

Deep learning for cancer histopathology training conflicts with privacy constraints in clinical settings. Federated Learning (FL) mitigates this by ke...

Onco-Seg: Adapting Promptable Concept Segmentation for Multi-Modal Medical Imaging

Medical image segmentation remains a critical bottleneck in clinical workflows, from diagnostic radiology to radiation oncology treatment planning. We...

Crosstalk of glutamine metabolism between cancer-associated fibroblasts and cancer cells.

Glutamine (Gln), a critical metabolic substrate, fuels the uncontrolled proliferation of cancer cells. Cancer-associated fibroblasts (CAFs), essential...

Modeling multi-scale uncertainty with evidence integration for reliable polyp segmentation.

Polyp segmentation is critical in medical image analysis. Traditional methods, while capable of producing precise outputs in well-defined regions, oft...

Photoacoustic-Integrated Multimodal Approach for Colorectal Cancer Diagnosis.

Colorectal cancer remains a major global health challenge, emphasizing the need for advanced diagnostic tools that enable early and accurate detection...