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A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology

IEEE transactions on medical imaging, 2017-07, Vol.36 (7), p.1550-1560

2017

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IEEE_Xplore

MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge

IEEE transactions on medical imaging, 2021-12, Vol.40 (12), p.3413-3423

2021

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IEEE_Xplore

Novel MRI deformation-heterogeneity radiomic features are associated with molecular subgroups and overall survival in pediatric medulloblastoma: Preliminary findings from a multi-institutional study

Frontiers in oncology, 2022-12, Vol.12, p.915143-915143

2022

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Convolutional neural networks for wavelet domain super resolution

Pattern recognition letters, 2017-04, Vol.90, p.65-71

2017

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TAMI-22. SEGMENTATION OF DISTINCT TUMOR HALLMARKS OF GLIOBLASTOMA ON DIGITAL HISTOPATHOLOGY USING A HIERARCHICAL DEEP LEARNING APPROACH

Neuro-oncology (Charlottesville, Va.), 2021-11, Vol.23 (Suppl 6), p.vi202-vi203

2021

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Some Properties of Range Operators on LCA Groups

Kragujevac Journal of Mathematics, 2023, Vol.47 (7), p.995

2023

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The Pursuit of Generalizability to Enable Clinical Translation of Radiomics

Radiology. Artificial intelligence, 2021-01, Vol.3 (1), p.e200227-e200227

2021

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NIMG-60. PREDICTING OVERALL SURVIVAL IN GLIOBLASTOMA USING HISTOPATHOLOGY VIA AN END-TO-END DEEP LEARNING PIPELINE: A LARGE MULTI-COHORT STUDY

Neuro-oncology (Charlottesville, Va.), 2021-11, Vol.23 (Supplement_6), p.vi143-vi143

2021

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Author's Reply to "MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge"

IEEE transactions on medical imaging, 2022-04, Vol.41 (4), p.1000-1003

2022

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IEEE_Xplore

A Multi-Organ Nucleus Segmentation Challenge

IEEE transactions on medical imaging, 2020-05, Vol.39 (5), p.1380-1391

2020

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IEEE_Xplore

Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors

The Journal of pathology, 2022-05, Vol.257 (1), p.17-28

2022

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Clinico-epidemiological profile of ENT cases at tertiary care center of Western UP: An institutional retrospective study

Annals of Indian Academy of Otorhinolaryngology Head and Neck Surgery, 2023-07, Vol.7 (2), p.25

2023

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Technical Note: MRQy — An open‐source tool for quality control of MR imaging data

Medical physics (Lancaster), 2020-12, Vol.47 (12), p.6029-6038

2020

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Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish s tage II and IV colon tumors

The Journal of pathology, 2022-05, Vol.257 (1), p.17-28

2022

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Abstract 3: Deep learning-based integration of esophageal cancer morphology with genomics

Cancer research (Chicago, Ill.), 2021-07, Vol.81 (13_Supplement), p.3-3

2021

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Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma

Clinical cancer research, 2020-04, Vol.26 (8), p.1866-1876

2020

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Using Bayesian Neural Networks to Select Features and Compute Credible Intervals for Personalized Survival Prediction

IEEE transactions on biomedical engineering, 2023-12, Vol.PP (12), p.1-18

2023

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IEEE_Xplore

NIMG-54. RADIOMIC FEATURES FROM LESION HABITAT PREDICT RESPONSE TO COMBINATION OF NIVOLUMAB AND BEVACIZUMAB IN PATIENTS WITH RECURRENT GLIOBLASTOMA: A FEASIBILITY STUDY

Neuro-oncology (Charlottesville, Va.), 2021-11, Vol.23 (Supplement_6), p.vi141-vi141

2021

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TAMI-22. SEGMENTATION OF DISTINCT TUMOR HALLMARKS OF GLIOBLASTOMA ON DIGITAL HISTOPATHOLOGY USING A HIERARCHICAL DEEP LEARNING APPROACH

Neuro-oncology (Charlottesville, Va.), 2021-11, Vol.23 (Supplement_6), p.vi202-vi203

2021

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Reproducibility analysis of multi‐institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset

Medical physics (Lancaster), 2020-12, Vol.47 (12), p.6039-6052

2020

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NIMG-26. RADIOMIC FEATURES OF GLIOBLASTOMA ON PRE-TREATMENT GD-T1W MRI ARE PREDICTIVE OF RESPONSE TO CHEMO-RADIATION THERAPY AND ASSOCIATED WITH AKT AND APOPTOSIS PATHWAYS

Neuro-oncology (Charlottesville, Va.), 2018-11, Vol.20 (suppl_6), p.vi181-vi181

2018

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FORMULATION AND IN VITRO EVALUATION OF ETHANOLIC EXTRACT OF POLYHERBAL FACE CREAM

International journal of current pharmaceutical research, 2022-03, p.41-47

2022

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Tumor Habitat-derived Radiomic Features at Pretreatment MRI That Are Prognostic for Progression-free Survival in Glioblastoma Are Associated with Key Morphologic Attributes at Histopathologic Examination: A Feasibility Study

Radiology. Artificial intelligence, 2020-11, Vol.2 (6), p.e190168-e190168

2020

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NEIM-01. PREDICTION OF RESPONSE TO COMBINATION OF NIVOLUMAB AND BEVACIZUMAB IN PATIENTS WITH RECURRENT GLIOBLASTOMA VIA RADIOMIC ANALYSIS ON CLINICAL MRI SCANS

Neuro-oncology advances, 2021-09, Vol.3 (Supplement_4), p.iv6-iv6

2021

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Detecting multiple sub-types of breast cancer in a single patient

2016 IEEE International Conference on Image Processing (ICIP), 2016, p.2648-2652

2016

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IEEE_Xplore

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A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology

MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge

Novel MRI deformation-heterogeneity radiomic features are associated with molecular subgroups and overall survival in pediatric medulloblastoma: Preliminary findings from a multi-institutional study

Convolutional neural networks for wavelet domain super resolution

TAMI-22. SEGMENTATION OF DISTINCT TUMOR HALLMARKS OF GLIOBLASTOMA ON DIGITAL HISTOPATHOLOGY USING A HIERARCHICAL DEEP LEARNING APPROACH

Some Properties of Range Operators on LCA Groups

The Pursuit of Generalizability to Enable Clinical Translation of Radiomics

NIMG-60. PREDICTING OVERALL SURVIVAL IN GLIOBLASTOMA USING HISTOPATHOLOGY VIA AN END-TO-END DEEP LEARNING PIPELINE: A LARGE MULTI-COHORT STUDY

Author's Reply to "MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge"

A Multi-Organ Nucleus Segmentation Challenge

Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish stage II and IV colon tumors

Clinico-epidemiological profile of ENT cases at tertiary care center of Western UP: An institutional retrospective study

Technical Note: MRQy — An open‐source tool for quality control of MR imaging data

Computer‐extracted features of nuclear morphology in hematoxylin and eosin images distinguish s tage II and IV colon tumors

Abstract 3: Deep learning-based integration of esophageal cancer morphology with genomics

Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma

Using Bayesian Neural Networks to Select Features and Compute Credible Intervals for Personalized Survival Prediction

NIMG-54. RADIOMIC FEATURES FROM LESION HABITAT PREDICT RESPONSE TO COMBINATION OF NIVOLUMAB AND BEVACIZUMAB IN PATIENTS WITH RECURRENT GLIOBLASTOMA: A FEASIBILITY STUDY

TAMI-22. SEGMENTATION OF DISTINCT TUMOR HALLMARKS OF GLIOBLASTOMA ON DIGITAL HISTOPATHOLOGY USING A HIERARCHICAL DEEP LEARNING APPROACH

Reproducibility analysis of multi‐institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset

NIMG-26. RADIOMIC FEATURES OF GLIOBLASTOMA ON PRE-TREATMENT GD-T1W MRI ARE PREDICTIVE OF RESPONSE TO CHEMO-RADIATION THERAPY AND ASSOCIATED WITH AKT AND APOPTOSIS PATHWAYS

FORMULATION AND IN VITRO EVALUATION OF ETHANOLIC EXTRACT OF POLYHERBAL FACE CREAM

Tumor Habitat-derived Radiomic Features at Pretreatment MRI That Are Prognostic for Progression-free Survival in Glioblastoma Are Associated with Key Morphologic Attributes at Histopathologic Examination: A Feasibility Study

NEIM-01. PREDICTION OF RESPONSE TO COMBINATION OF NIVOLUMAB AND BEVACIZUMAB IN PATIENTS WITH RECURRENT GLIOBLASTOMA VIA RADIOMIC ANALYSIS ON CLINICAL MRI SCANS

Detecting multiple sub-types of breast cancer in a single patient

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