Colon Cancer

DepthPilot: From Controllability to Interpretability in Colonoscopy Video Generation

Controllable medical video generation has achieved remarkable progress, but it still lacks interpretability, which requires the alignment of generated contents with physical priors and faithful clinical manifestations. To push the boundaries from mere controllability to interpretability, we propose DepthPilot, the first interpretable framework for colonoscopy video generation. This work takes a st...

Multi-Modal Deep Learning Integrates Spatial Topologies and Sequential Motifs to Identify Class I HDAC Inhibitors as Pan-Cancer Therapeutics

The molecular characterization of human solid growths has introduced immense genomic complexity and intra-tumoral diversification. Converting these detailed, multi-omic profiles right into workable, broad-spectrum therapeutics continues to be an awesome traffic jam in accuracy oncology. Traditional computational drug repurposing strategies largely rely on single-modality chemical descriptors, whic...

Interpreting and Validating a Deep Learning Model Predictive of Spatial Morphologic-Molecular Patterns in Lung Adenocarcinoma, Using Ground Truth Immunohistochemistry

Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer, exhibits profound histological and molecular heterogeneity. While g...

Attention-based multiple instance learning for predominant growth pattern prediction in lung adenocarcinoma wsi using foundation models

Lung adenocarcinoma (LUAD) grading depends on accurately identifying growth patterns, which are indicators of prognosis and can influence treatment de...

Sharpening Lightweight Models for Generalized Polyp Segmentation: A Boundary Guided Distillation from Foundation Models

Automated polyp segmentation is critical for early colorectal cancer detection and its prevention, yet remains challenging due to weak boundaries, lar...

SCOPE: Integrating Organoid Screening and Clinical Variables Through Machine Learning for Cancer Trial Outcome Prediction

BackgroundPredicting whether a treatment will demonstrate meaningful clinical benefit before committing to a large-scale trial remains a major unmet n...

TwinTrack: Post-hoc Multi-Rater Calibration for Medical Image Segmentation

Pancreatic ductal adenocarcinoma (PDAC) segmentation on contrast-enhanced CT is inherently ambiguous: inter-rater disagreement among experts reflects ...

Dual-Modal Lung Cancer AI: Interpretable Radiology and Microscopy with Clinical Risk Integration

Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Conventional computed tomography (CT) imaging, while essential fo...

ASGNet: Adaptive Spectrum Guidance Network for Automatic Polyp Segmentation

Early identification and removal of polyps can reduce the risk of developing colorectal cancer. However, the diverse morphologies, complex backgrounds...

Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors

This study evaluates colorectal risk factors and compares classical models against Quantum Neural Networks (QNNs) for anastomotic leak prediction. Ana...

Integrating computational chemistry and machine learning to predict KRAS mutation-induced resistance

Mutation-induced drug resistance is a major contributor to the failure of targeted cancer therapies, particularly in tumors driven by mutations in the...

Virtual Spectral Decomposition with Dendritic Tile Selection: An Explainable AI Framework for Multimodal Tissue Composition Analysis and Immune Phenotyping Across Pancreatic, Lung, and Breast Cancer

Background: Current deep learning models in computational pathology, radiology, and digital pathology produce opaque predictions that lack the explain...

OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA

The tumor microenvironment (TME) plays a central role in cancer progression, treatment response, and patient outcomes, yet large-scale, consistent, an...

Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models

Occlusion, where target structures are partially hidden by surgical instruments or overlapping tissues, remains a critical yet underexplored challenge...

Benchmarking Deep Learning for Future Liver Remnant Segmentation in Colorectal Liver Metastasis

Accurate segmentation of the future liver remnant (FLR) is critical for surgical planning in colorectal liver metastases (CRLM) to prevent fatal post-...

Improving Controllable Generation: Faster Training and Better Performance via $x_0$-Supervision

Text-to-Image (T2I) diffusion/flow models have recently achieved remarkable progress in visual fidelity and text alignment. However, they remain limit...

MorphDistill: Distilling Unified Morphological Knowledge from Pathology Foundation Models for Colorectal Cancer Survival Prediction

Background: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. Accurate survival prediction is essential for treat...

Advancements and limitations of image-enhanced endoscopy in colorectal lesion diagnosis and treatment selection: A narrative review.

Colorectal cancer (CRC) is a leading cause of cancer-related mortality, highlighting the need for early detection and accurate lesion characterization...

Aging Signals on Chest Radiographs: Association of Chest Radiograph-Derived Age Acceleration With Future Lung Cancer Incidence

Purpose: To evaluate whether chest radiograph-derived age acceleration is associated with incident lung cancer and whether it improves discrimination ...

Integrated single-cell and bulk transcriptomic analysis leverages liver metastasis-related genes to develop a prognostic model for colorectal cancer patients

Based on single-cell RNA sequencing data, differentially expressed genes (LMR DEGs) between colorectal cancer liver metastasis epithelium and primary ...