G. H. Chang

发表

Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification

Xiaoping Zhou, V. Kolachalama, R. Au, 2020, Brain : a journal of neurology.

Development and validation of a deep learning framework for Alzheimer’s disease classification

V. Kolachalama, R. Au, S. Auerbach, 2019, bioRxiv.

Assessment of knee pain from MR imaging using a convolutional Siamese network

D. Felson, V. Kolachalama, T. Capellini, 2020, European Radiology.

Predicting bilateral knee pain from MR imaging using deep neural networks

D. Felson, V. Kolachalama, T. Capellini, 2018 .

Evolutionary Selection and Constraint on Human Knee Chondrocyte Regulation Impacts Osteoarthritis Risk

Neil T. Roach, V. Kolachalama, T. Capellini, 2020, Cell.

Fusion of deep learning models of MRI scans, Mini–Mental State Examination, and logical memory test enhances diagnosis of mild cognitive impairment

Vijaya B. Kolachalama, Rhoda Au, Shangran Qiu, 2018, Alzheimer's & dementia.

Uremic Solute-Aryl Hydrocarbon Receptor-Tissue Factor Axis Associates with Thrombosis after Vascular Injury in Humans.

Mostafa E. Belghasem, V. Kolachalama, K. Ravid, 2018, Journal of the American Society of Nephrology : JASN.

Pairwise learning of MRI scans using a convolutional Siamese network for prediction of knee pain

D. Felson, V. Kolachalama, T. Capellini, 2019 .

Assessment of knee pain from MRI scans using a convolutional Siamese network

Ali Guermazi, Vijaya B. Kolachalama, David T. Felson, 2019 .

Subchondral Bone Length in Knee Osteoarthritis: A Deep Learning–Derived Imaging Measure and Its Association With Radiographic and Clinical Outcomes

D. Felson, V. Kolachalama, T. Capellini, 2021, medRxiv.