In recent years there has been an increased thrust to understand and quantify the complex information conveyed by medical images. Developing modern computational techniques that offer the potential for extracting diverse and complex information from imaging data and applying these to a plethora of clinical studies is crucial. These techniques not only support precise quantification but also overcome the limitations of subjective visual interpretation. Furthermore, these methods can facilitate finding specific markers that relate to pathologies as well as aid in treatment planning.
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Anatomically Parameterized Statistical Shape Model: Explaining Morphometry through Statistical Learning Authors: Arnaud Boutillon, Asma Salhi, Valérie Burdin, Bhushan Borotikar
Multimodal Brain Connectomics-Based Prediction of Parkinson’s Disease Using Graph Attention Networks Authors: Apoorva Safai, Nirvi Vakharia, Shweta Prasad, Jitender Saini, Apurva Shah, Abhishek Lenka, Pramod Kumar Pal, Madhura Ingalhalikar
Neurite orientation dispersion and density imaging in cocaine use disorder Authors: Jalil Rasgado-Toledo, Apurva Shah, Madhura Ingalhalikar, Eduardo A Garza-Villarreal
A Musculoskeletal Model Customized for Sagittal and Frontal Knee Kinematics with Improved Knee Joint Stability
Authors: Shivangi Giri, RP Tewari, Asma Salhi, Mathieu Lempereur, Bhushan Borotikar
Disrupted structural connectome and neurocognitive functions in Duchenne muscular dystrophy: classifying and subtyping based on Dp140 dystrophin isoform
Authors: Veeramani Preethish-Kumar, Apurva Shah, Kiran Polavarapu, Manoj Kumar, Apoorva Safai, Seena Vengalil
