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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Dynamic multi feature-class Gaussian process models Authors: Jean-Rassaire Fouefack, Bhushan Borotikar, Marcel Lüthi, Tania S Douglas, Valérie Burdin, Tinashe EM Mutsvangwa
3D reconstruction of joints from partial data using multi-object-based model: Towards a patient-specific knee implant design Authors: Jean-Rassaire Fouefack, Guillaume Dardenne, Tinashe Bhushan Borotikar, EM Mutsvangwa, Valérie Burdin
Federated learning enables big data for rare cancer boundary detection Authors: Sarthak Pati, Ujjwal Baid, Brandon Edwards, Micah Sheller, Shih-Han Wang, G Anthony Reina, Patrick Foley, Alexey Gruzdev, Deepthi Karkada, Christos Davatzikos, Chiharu Sako, ...
Editorial: It Is a Matter of Matters: Deciphering Structural and Functional Brain Connectivity Authors: Gopikrishna Deshpande, Vinoo Alluri, Aaryana Sharma and Madhura Ingalhalikar
Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors Authors: Arnaud Boutillon, Pierre-Henri Conze, Christelle Pons, Valérie Burdin, Bhushan Borotikar
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