Pallavi Tiwari
Orcid: 0000-0001-9477-4856
According to our database1,
Pallavi Tiwari
authored at least 47 papers
between 2007 and 2025.
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Bibliography
2025
Cyst-X: AI-Powered Pancreatic Cancer Risk Prediction from Multicenter MRI in Centralized and Federated Learning.
CoRR, July, 2025
3D Nephrographic Image Synthesis in CT Urography with the Diffusion Model and Swin Transformer.
CoRR, February, 2025
Medical Image Anal., 2025
Proceedings of the 22nd IEEE International Symposium on Biomedical Imaging, 2025
Proceedings of the 22nd IEEE International Symposium on Biomedical Imaging, 2025
2024
ResNCT: A Deep Learning Model for the Synthesis of Nephrographic Phase Images in CT Urography.
CoRR, 2024
Human-in-the-Loop (HITL) Learning for Identifying Glioblastoma Hallmarks on H&E Slides.
Proceedings of the IEEE International Symposium on Biomedical Imaging, 2024
Multi-Scale Co-Attention Transformer Model to Integrate Radiology, Histology, and Genomics: Application to Survival Prediction in Glioblastoma.
Proceedings of the IEEE International Symposium on Biomedical Imaging, 2024
Graph-Radiomics Learning (GrRAiL): Application to Distinguishing Glioblastoma Recurrence from Pseudo-Progression on Structural MRI.
Proceedings of the IEEE International Symposium on Biomedical Imaging, 2024
2023
Self-supervised deep learning to predict molecular markers from routine histopathology slides for high-grade glioma tumors.
Proceedings of the Medical Imaging 2023: Digital and Computational Pathology, 2023
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 hierarchical deep learning approach for segmentation of glioblastoma tumor niches on digital histopathology.
Proceedings of the Medical Imaging 2022: Digital and Computational Pathology, 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, San Diego, 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, San Diego, 2019
Deformation heterogeneity radiomics to predict molecular subtypes of pediatric Medulloblastoma on routine MRI.
Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, San Diego, 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, San Diego, 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, San Diego, 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, San Diego, 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