Payabvash Lab GitHub

Payabvash has published the codes for below papers in our GitHub lab.

Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography. Tran AT, Desser D, Zeevi T, Abou Karam G, Zietz J, Dell'Orco A, Chen MC, Malhotra A, Qureshi AI, Murthy SB, Majidi S, Falcone GJ, Sheth KN, Nawabi J, Payabvash S.Appl Sci (Basel). 2025 Jan;15(1):111. doi: 10.3390/app15010111.

Improving the Robustness of Deep Learning Models in Predicting Hematoma Expansion from Admission Head CT.

Tran AT, Abou Karam G, Zeevi D, Qureshi AI, Malhotra A, Majidi S, Murthy SB, Park S, Kontos D, Falcone GJ, Sheth KN, Payabvash S.AJNR Am J Neuroradiol. 2025 Jul 1;46(7):1404-1411. doi: 10.3174/ajnr.A8650.

CTA Radiomics Data

CTA_radiomics_2022.zip

The four compressed .csv files provide the values of all extracted radiomics features for the Yale and Geisinger datasets described in Avery et al., 2022. These radiomics features were extracted from the bilateral middle cerebral artery (MCA) territories of each patient’s admission CTA. A separate file is provided for discharge (short-term) and 3-month (long-term) outcome cohorts for the Yale training/cross-validation (CV) dataset, independent Yale dataset, and external Geisinger dataset (3-month – long-term – outcome cohort only). The files are titled accordingly and include:
Radiomics_YaleTrainingCV_ShortTermFollowUP.csv

Radiomics _YaleTrainingCV_LongTermFollowUP.csv
Radiomics _YaleIndependent_ShortTermFollowUP.csv
Radiomics _YaleIndependent_LongTermFollowUP.csv
Radiomics _Geisinger_LongTermFollowUP.csv