Phenotyping neurodegeneration using machine learning:
We build machine learning frameworks to process multimodal data and identify specific signatures of neurodegeneration. We have experience in dealing with large data cohorts such as the Framingham Heart Study and established several computational pipelines to efficiently process volumetric images of the brain, neuropathology and other modes of data and use them for further analysis. Click on Papers for full list.
- Qiu S, Joshi PS, Miller MI, Xue C, Zhou X, Karjadi C, Chang GH, Joshi AS, Dwyer B, Zhu S, Kaku M, Zhou Y, Alderazi YJ, Swaminathan A, Kedar S, Saint-Hilaire M-H, Auerbach SH, Yuan J, Sartor EA, Au R, Kolachalama VB. Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification. Brain. 2020 Jun 1;143(6):1920-1933.
- Joshi PS, Heydari M, Kannan S, Ang TFA, Qin Q, Liu X, Mez J, Devine S, Au R, Kolachalama VB. Temporal association of neuropsychological test performance using unsupervised learning reveals a distinct signature of Alzheimer’s disease status. Alzheimers Dement (N Y). 2019 Dec 28;5:964-973.
- Qiu S, Chang GH, Panagia M, Gopal DM, Au R, Kolachalama VB. Fusion of deep learning models of MRI scans, mini-mental state examination and logical memory test enhances diagnosis of mild cognitive impairment. Alzheimers Dement (Amst). 2018 Sep 28;10:737-749.
We develop computational frameworks based on deep learning to assist the pathologist. Our current application areas include kidney disease, lung cancer and colorectal cancer. Click on Papers for full list.
- Kannan S, Morgan LA, Liang B, Cheung MG, Lin CQ, Mun D, Nader RG, Belghasem ME, Henderson JM, Francis JM, Chitalia VC, Kolachalama VB. Segmentation of Glomeruli Within Trichrome Images Using Deep Learning. Kidney Int Rep. 2019 Apr 15;4(7):955-962.
- Kumaradevan S, Lee SY, Richards S, Lyle C, Zhao Q, Tapan U, Jiangliu Y, Ghumman S, Walker J, Belghasem M, Arinze N, Kuhnen A, Weinberg J, Francis J, Hartshorn K, Kolachalama VB, Cifuentes D, Rahimi N, Chitalia VC. c-Cbl Expression Correlates with Human Colorectal Cancer Survival and Its Wnt/β-Catenin Suppressor Function Is Regulated by Tyr371 Phosphorylation. Am J Pathol. 2018 Aug;188(8):1921-1933.
- Kolachalama VB, Singh P, Lin CQ, Mun D, Belghasem ME, Henderson JM, Francis JM, Salant DJ, Chitalia VC. Association of Pathological Fibrosis with Renal Survival Using Deep Neural Networks. Kidney Int Rep. 2018 Jan 11;3(2):464-475.
We develop machine learning frameworks to bring efficiency to the analysis of large-scale studies such as Osteoarthritis Initiative (OAI) and Multicenter osteoarthritis (MOST) study. We are particularly interested to quantify structures that are responsible for pain and factors that contribute to the progression of knee osteoarthritis. Click on Papers for full list.
- Chang GH, Felson DT, Qiu S, Guermazi A, Capellini TD, Kolachalama VB. Assessment of knee pain from MR imaging using a convolutional Siamese network. Eur Radiol. 2020 Jun;30(6):3538-3548.
- Richard D, Liu Z, Cao J, Kiapour AM, Willen J, Yarlagadda S, Jagoda E, Kolachalama VB, Sieker JT, Chang GH, Muthuirulan P, Young M, Masson A, Konrad J, Hosseinzadeh S, Maridas DE, Rosen V, Krawetz R, Roach N, Capellini TD. Evolutionary Selection and Constraint on Human Knee Chondrocyte Regulation Impacts Osteoarthritis Risk. Cell. 2020 Apr 16;181(2):362-381.e28.