Radiomics and Machine Learning in Medical Imaging

Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning

The paper uses machine learning to connect proteomics data with histopathology images in kidney cancer, finding correlations between proteins and imaging-based predictions.

Library of model implementations for sharing deep-learning image segmentation and outcomes models

The paper presents an open-source library of deep-learning models for image segmentation and outcomes in oncology, enabling validation, automation, and integration into clinical workflows.

Integrative framework of cross-module deep biomarker for the prognosis of clear cell renal cell carcinoma: A retrospective TCGA project

The paper develops an integrative deep learning framework combining imaging and gene data to predict prognosis in ccRCC, showing improved performance over individual biomarkers.

Integrating deep and radiomics features in cancer bioimaging

The paper introduces a framework combining deep learning and radiomics for cancer prognosis using CT and PET imaging, showing improved accuracy over single-method approaches.

CNNcon: A Quantitative Imaging Tool for Lung CT Image Feature Analysis

CNNcon is a Docker-based tool for analyzing lung CT images to predict malignancy and extract features for further analysis.

Computed Tomography Measures of Inter-site tumor Heterogeneity for Classifying Outcomes in High-Grade Serous Ovarian Carcinoma: a Retrospective Study

This study develops CT-based texture analysis to quantify intra- and inter-site tumor heterogeneity in HGSOC and shows it correlates with progression-free survival.

Locoregional Radiogenomic Models Capture Gene Expression Heterogeneity in Glioblastoma

This paper develops locoregional radiogenomic models to capture gene expression heterogeneity in glioblastoma using multi-omics data fusion and image analysis.

Multiple Kernel Learning Approach For Medical Image Analysis

The paper applies multiple kernel learning algorithms to analyze high-dimensional and heterogeneous medical image data for diagnostic purposes.