Jun Cheng

TL;DR: The augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types.

TL;DR: This paper proposes a novel feature extraction framework for retrieving brain tumors in T1-weighted contrast-enhanced MRI images and demonstrates the power of the proposed algorithm against some related state-of-the-art methods on the same dataset.

TL;DR: An integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma is presented, extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome.

TL;DR: This work was supported by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China, which had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

TL;DR: Wang et al. as discussed by the authors proposed a novel bioimage informatics pipeline for automatically characterizing the topological organization of different cell patterns in the tumor microenvironment, and applied this pipeline to the only publicly available large histopathology image dataset for a cohort of 190 patients with papillary renal cell carcinoma.