Chengkun Sun
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View article: Multimodal Prediction of Renal Tumor Malignancy from Radiology Reports and Structured Electronic Health Records: Retrospective Cohort Study (Preprint)
Multimodal Prediction of Renal Tumor Malignancy from Radiology Reports and Structured Electronic Health Records: Retrospective Cohort Study (Preprint) Open
BACKGROUND Accurate preoperative prediction of renal tumor malignancy is essential but remains challenging. While radiology and structured electronic health record (EHR) data are widely used for tumor evaluation, radiology reports—though …
View article: Predicting Nephrectomy Risk in Patients with Renal Cancer Using Real-World Electronic Health Records
Predicting Nephrectomy Risk in Patients with Renal Cancer Using Real-World Electronic Health Records Open
Nephrectomy, the surgical removal of a kidney, is a critical treatment for renal cancer, and predicting its likelihood can help guide clinical decision-making and optimize preoperative planning. This study utilized real-world electronic he…
View article: Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study
Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study Open
Background Colorectal cancer is now the leading cause of cancer-related deaths among young Americans. Accurate early prediction and a thorough understanding of the risk factors for early-onset colorectal cancer (EOCRC) are vital for effect…
View article: Beyond Skip Connection: Pooling and Unpooling Design for Elimination Singularities
Beyond Skip Connection: Pooling and Unpooling Design for Elimination Singularities Open
Training deep Convolutional Neural Networks (CNNs) presents unique challenges, including the pervasive issue of elimination singularities—consistent deactivation of nodes leading to degenerate manifolds within the loss landscape. These sin…
View article: BGDB: Bernoulli-Gaussian Decision Block with Improved Denoising Diffusion Probabilistic Models
BGDB: Bernoulli-Gaussian Decision Block with Improved Denoising Diffusion Probabilistic Models Open
Generative models can enhance discriminative classifiers by constructing complex feature spaces, thereby improving performance on intricate datasets. Conventional methods typically augment datasets with more detailed feature representation…
View article: From Image to Report: Automating Lung Cancer Screening Interpretation and Reporting with Vision-Language Models
From Image to Report: Automating Lung Cancer Screening Interpretation and Reporting with Vision-Language Models Open
View article: MRISeqClassifier: A Deep Learning Toolkit for Precise MRI Sequence Classification
MRISeqClassifier: A Deep Learning Toolkit for Precise MRI Sequence Classification Open
Magnetic Resonance Imaging (MRI) is a crucial diagnostic tool in medicine, widely used to detect and assess various health conditions. Different MRI sequences, such as T1-weighted, T2-weighted, and FLAIR, serve distinct roles by highlighti…
View article: Beyond Skip Connection: Pooling and Unpooling Design for Elimination Singularities
Beyond Skip Connection: Pooling and Unpooling Design for Elimination Singularities Open
Training deep Convolutional Neural Networks (CNNs) presents unique challenges, including the pervasive issue of elimination singularities, consistent deactivation of nodes leading to degenerate manifolds within the loss landscape. These si…
View article: GASA-UNet: Global Axial Self-Attention U-Net for 3D Medical Image Segmentation
GASA-UNet: Global Axial Self-Attention U-Net for 3D Medical Image Segmentation Open
Accurate segmentation of multiple organs and the differentiation of pathological tissues in medical imaging are crucial but challenging, especially for nuanced classifications and ambiguous organ boundaries. To tackle these challenges, we …
View article: BGDB: Bernoulli-Gaussian Decision Block with Improved Denoising Diffusion Probabilistic Models
BGDB: Bernoulli-Gaussian Decision Block with Improved Denoising Diffusion Probabilistic Models Open
Generative models can enhance discriminative classifiers by constructing complex feature spaces, thereby improving performance on intricate datasets. Conventional methods typically augment datasets with more detailed feature representation…