Pallavi Tiwari
Orcid: 0000-0001-9477-4856
According to our database1,
Pallavi Tiwari
authored at least 35 papers
between 2007 and 2022.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
On csauthors.net:
Bibliography
2022
Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma.
IEEE Trans. Medical Imaging, 2022
RADIomic Spatial TexturAl Descriptor (RADISTAT): Quantifying Spatial Organization of Imaging Heterogeneity Associated With Tumor Response to Treatment.
IEEE J. Biomed. Health Informatics, 2022
A radiomics approach to distinguish non-contrast enhancing tumor from vasogenic edema on multi-parametric pre-treatment MRI scans for glioblastoma tumors.
Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, 2022
Proceedings of the Advances in Computing and Data Sciences - 6th International Conference, 2022
2020
Can Tumor Location on Pre-treatment MRI Predict Likelihood of Pseudo-Progression vs. Tumor Recurrence in Glioblastoma? - A Feasibility Study.
Frontiers Comput. Neurosci., 2020
Can tumor location on pre-treatment MRI predict likelihood of pseudo-progression versus tumor recurrence in Glioblastoma? A feasibility study.
CoRR, 2020
Spatial-And-Context Aware (SpACe) "Virtual Biopsy" Radiogenomic Maps to Target Tumor Mutational Status on Structural MRI.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020
Combining deep and hand-crafted MRI features for identifying sex-specific differences in autism spectrum disorder versus controls.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020
"Lesion-habitat" radiomics to distinguish radiation necrosis from tumor recurrence on post-treatment MRI in metastatic brain tumors.
Proceedings of the Medical Imaging 2020: Computer-Aided Diagnosis, 2020
2019
Proceedings of the Radiomics and Radiogenomics in Neuro-oncology, 2019
STructural Rectal Atlas Deformation (StRAD) Features for Characterizing Intra- and Peri-wall Chemoradiation Response on MRI.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2019, 2019
Radiomics of the lesion habitat on pre-treatment MRI predicts response to chemo-radiation therapy in Glioblastoma.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019
Deformation heterogeneity radiomics to predict molecular subtypes of pediatric Medulloblastoma on routine MRI.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019
Radiogenomic characterization of response to chemo-radiation therapy in glioblastoma is associated with PI3K/AKT/mTOR and apoptosis signaling pathways.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, 2019
2017
Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases.
BMC Medical Imaging, 2017
Radiographic-Deformation and Textural Heterogeneity (r-DepTH): An Integrated Descriptor for Brain Tumor Prognosis.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, 2017
RADIomic Spatial TexturAl descripTor (RADISTAT): Characterizing Intra-tumoral Heterogeneity for Response and Outcome Prediction.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, 2017
Radiogenomic analysis of hypoxia pathway reveals computerized MRI descriptors predictive of overall survival in glioblastoma.
Proceedings of the Medical Imaging 2017: Computer-Aided Diagnosis, 2017
2014
Identifying MRI markers to evaluate early treatment-related changes post-laser ablation for cancer pain management.
Proceedings of the Medical Imaging 2014: Image-Guided Procedures, 2014
Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): Distinguishing Tumor Confounders and Molecular Subtypes on MRI.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014, 2014
Texture descriptors to distinguish radiation necrosis from recurrent brain tumors on multi-parametric MRI.
Proceedings of the Medical Imaging 2014: Computer-Aided Diagnosis, 2014
2013
Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS.
Medical Image Anal., 2013
Quantitative evaluation of multi-parametric MR imaging marker changes post-laser interstitial ablation therapy (LITT) for epilepsy.
Proceedings of the Medical Imaging 2013: Image-Guided Procedures, 2013
2011
A magnetic resonance spectroscopy driven initialization scheme for active shape model based prostate segmentation.
Medical Image Anal., 2011
Weighted Combination of Multi-Parametric MR Imaging Markers for Evaluating Radiation Therapy Related Changes in the Prostate.
Proceedings of the Prostate Cancer Imaging. Image Analysis and Image-Guided Interventions, 2011
Variable Ranking with PCA: Finding Multiparametric MR Imaging Markers for Prostate Cancer Diagnosis and Grading.
Proceedings of the Prostate Cancer Imaging. Image Analysis and Image-Guided Interventions, 2011
CADOnc ©: An integrated toolkit for evaluating radiation therapy related changes in the prostate using multiparametric MRI.
Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011
Multi-modal data fusion schemes for integrated classification of imaging and non-imaging biomedical data.
Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011
2010
Semi Supervised Multi Kernel (SeSMiK) Graph Embedding: Identifying Aggressive Prostate Cancer via Magnetic Resonance Imaging and Spectroscopy.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention, 2010
2009
Spectral Embedding Based Probabilistic Boosting Tree (ScEPTre): Classifying High Dimensional Heterogeneous Biomedical Data.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention, 2009
2008
A multi-modal prostate segmentation scheme by combining spectral clustering and active shape models.
Proceedings of the Medical Imaging 2008: Image Processing, 2008
Consensus-Locally Linear Embedding (C-LLE): Application to Prostate Cancer Detection on Magnetic Resonance Spectroscopy.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention, 2008
A meta-classifier for detecting prostate cancer by quantitative integration of in vivo magnetic resonance spectroscopy and magnetic resonance imaging.
Proceedings of the Medical Imaging 2008: Computer-Aided Diagnosis, 2008
2007
A Hierarchical Unsupervised Spectral Clustering Scheme for Detection of Prostate Cancer from Magnetic Resonance Spectroscopy (MRS).
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007, 10th International Conference, Brisbane, Australia, October 29, 2007