EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, Featuring Prognostic Stratification Boosting

no code implementations • 26 Jan 2021 • Hassan Muhammad, Chensu Xie, Carlie S. Sigel, Michael Doukas, Lindsay Alpert, William R. Jarnagin, Amber Simpson, Thomas J. Fuchs

EPIC-Survival bridges encoding and aggregation into an end-to-end survival modelling approach, while introducing stratification boosting to encourage the model to not only optimize ranking, but also to discriminate between risk groups.

Clustering Survival Analysis +1

Beyond Classification: Whole Slide Tissue Histopathology Analysis By End-To-End Part Learning

no code implementations • MIDL 2019 • Chensu Xie, Hassan Muhammad, Chad M. Vanderbilt, Raul Caso, Dig Vijay Kumar Yarlagadda, Gabriele Campanella, Thomas J. Fuchs

A loss with respect to the slide label is backpropagated through an integrated CNN model to $k$ input tiles that are used to represent each part.

Multi-Label Classification whole slide images

Towards Unsupervised Cancer Subtyping: Predicting Prognosis Using A Histologic Visual Dictionary

no code implementations • 12 Mar 2019 • Hassan Muhammad, Carlie S. Sigel, Gabriele Campanella, Thomas Boerner, Linda M. Pak, Stefan Büttner, Jan N. M. IJzermans, Bas Groot Koerkamp, Michael Doukas, William R. Jarnagin, Amber Simpson, Thomas J. Fuchs

Combinations of these clusters were significant in multivariate analysis.

Clustering

Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations

no code implementations • 2 Aug 2016 • Peter J. Schüffler, Judy Sarungbam, Hassan Muhammad, Ed Reznik, Satish K. Tickoo, Thomas J. Fuchs

Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for understanding disease progression and for making informed treatment decisions.

Classification General Classification +1