Welcome by Hassan Mohy-ud-Din, PhD

Keynote 1: Robust & Explainable Deep Learning Algorithms for Radiological Systems by Ulas Bagci, PhD

Oral 1: Local binary and ternary patterns based quantitative texture analysis for assessment of IDH genotype in gliomas on multi-modal MRI

Sonal Gore, Tanay Chougule, Jitender Saini, Madhura Ingalhalikar, and Jayant Jagtap

Oral 2: Differentiation of recurrent glioblastoma from radiation necrosis using diffusion radiomics: Machine Learning model development and external validation

Yae Won Park, Ji Eun Park, Sung Soo Ahn, Hwiyoung Kim, Ho Sung Kim, and Seung-Koo Lee

Oral 3: Using functional Magnetic Resonance Imaging and personal characteristics features for detection of neurological conditions

Batool Rathore, Muhammad Awais, Muhammad Usama Usman, Imran Shafi, and Waqas Ahmed

Oral 4: Brain tumor survival prediction using radiomic features

Sobia Yousaf, Syed Muhammad Anwar, Harish RaviPrakash, and Ulas Bagci

Keynote 2: Radiomics & Radiogenomics with Deep Learning in Neuro-oncology by Jayashree Kalpathy-Cramer, PhD

Oral 5: Overall survival prediction in gliomas using region-specific radiomic features

Asma Shaheen, Stefano Burigat, Ulas Bagci, and Hassan Mohy-ud-Din

Oral 6: Radiogenomics of glioblastoma: Identification of radiomics associated with molecular subtypes

Navodini Wijethilake, Mobarakol Islam, Dulani Meedeniya, Charith Chitraranjan, Indika Perera, and Hongliang Ren

Oral 7: Automated multi-class brain tumor types detection by extracting RICA based features and employing machine learning techniques

Sadia Anjum, Lal Hussain, Mushtaq Ali, and Adeel Ahmed Abbasi

Keynote 3: Machine Learning and Glioma Imaging Biomarkers by Thomas C. Booth, MBBS, PhD

Closing Remarks by Dr. Hassan Mohy-ud-Din