A. Midya

发表

Edge Weighted Local Texture Features for the Categorization of Mammographic Masses

Jayasree Chakraborty, A. Sadhu, A. Midya, 2018 .

Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases

Amber L. Simpson, Lauryn B. Adams, M. Gonen, 2018, European Radiology.

CT radiomics to predict high‐risk intraductal papillary mucinous neoplasms of the pancreas

Amber L. Simpson, P. Allen, Jayasree Chakraborty, 2018, Medical physics.

Short-term reproducibility of radiomic features in liver parenchyma and liver malignancies on contrast-enhanced CT imaging

Abhishek Midya, Jayasree Chakraborty, Amber L. Simpson, 2018, Abdominal Radiology.

Texture analysis of gradient images for benign-malignant mass classification

Jayasree Chakraborty, A. Midya, Rinku Rabidas, 2017, 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN).

Recurrence After Resection of Pancreatic Cancer: Can Radiomics Predict Patients at Greatest Risk of Liver Metastasis?

Amber L. Simpson, M. Gönen, M. D'Angelica, 2022, Annals of Surgical Oncology.

Switchable video error concealment using encoder driven scene transition detection and edge preserving SEC

S. Sengupta, A. Midya, 2015, Multimedia Tools and Applications.

Video error concealment through 3-D face model

Abhishek Midya, Jayasree Chakraborty, Rajeev Ranjan, 2017, Multimedia Tools and Applications.

Detection of the nipple in mammograms with Gabor filters and the Radon transform

Rangaraj M. Rangayyan, Niranjan Khandelwal, Sudipta Mukhopadhyay, 2015, Biomed. Signal Process. Control..

Moving object detection using modified temporal differencing and local fuzzy thresholding

Debi Prosad Dogra, Partha Pratim Roy, Ashish Singh, 2017, The Journal of Supercomputing.

Dynamic background modeling using intensity and orientation distribution of video sequence

Jayasree Chakraborty, A. Midya, A. Sunaniya, 2019, Multimedia Tools and Applications.

Influence of CT acquisition and reconstruction parameters on radiomic feature reproducibility

Mithat Gönen, Abhishek Midya, Jayasree Chakraborty, 2018, Journal of medical imaging.

Quantitative Computed Tomography Image Analysis to Predict Pancreatic Neuroendocrine Tumor Grade.

Amber L. Simpson, Sharon A. Lawrence, M. Gonen, 2021, JCO Clinical Cancer Informatics.

Scene content driven FEC allocation for video streaming

Somnath Sengupta, Abhishek Midya, Rajeev Ranjan, 2014, Signal Process. Image Commun..

Neighborhood Structural Similarity Mapping for the Classification of Masses in Mammograms

Abhishek Midya, Jayasree Chakraborty, Rinku Rabidas, 2018, IEEE Journal of Biomedical and Health Informatics.

Computer-aided detection and diagnosis of mammographic masses using multi-resolution analysis of oriented tissue patterns

Abhishek Midya, Jayasree Chakraborty, Rinku Rabidas, 2018, Expert Syst. Appl..

Deep convolutional neural network for the classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma

Jian Zheng, Abhishek Midya, Amber L. Simpson, 2018, Medical Imaging.

Quantitative CT analysis for the preoperative prediction of pathologic grade in pancreatic neuroendocrine tumors

Mithat Gönen, Abhishek Midya, Amber L. Simpson, 2018, Medical Imaging.

Automatic characterization of masses in mammograms

Sudipta Mukhopadhyay, Abhishek Midya, Jayasree Chakraborty, 2013, 2013 6th International Conference on Biomedical Engineering and Informatics.

Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation

Abhishek Midya, Jayasree Chakraborty, Amber L. Simpson, 2019, European Radiology.

Computer-Aided Detection of Mammographic Masses Using Hybrid Region Growing Controlled by Multilevel Thresholding

Rangaraj M. Rangayyan, Sudipta Mukhopadhyay, Abhishek Midya, 2019 .

Computer-Aided Detection of Mammographic Masses Using Hybrid Region Growing Controlled by Multilevel Thresholding

R. Rangayyan, S. Mukhopadhyay, N. Khandelwal, 2018, Journal of Medical and Biological Engineering.

Face detection using skin color modeling and geometric feature

Abhishek Midya, Jayasree Chakraborty, Alok Verma, 2014, 2014 International Conference on Informatics, Electronics & Vision (ICIEV).

Computerized Diagnosis of Liver Tumors From CT Scans Using a Deep Neural Network Approach

Amber L. Simpson, M. Gonen, M. D'Angelica, 2023, IEEE Journal of Biomedical and Health Informatics.

A multi features based background modelling approach for moving object detection.

Jayasree Chakraborty, A. Midya, A. Sunaniya, 2022, Optik.

A Study of Different Texture Features Based on Local Operator for Benign-malignant Mass Classification☆

Wasim Arif, Abhishek Midya, Jayasree Chakraborty, 2016 .

Quantitative imaging features of pretreatment CT predict volumetric response to chemotherapy in patients with colorectal liver metastases

Amber L. Simpson, Lauryn B. Adams, M. Gonen, 2018, European Radiology.

Multi-Resolution Analysis of Edge-Texture Features for Mammographic Mass Classification

Wasim Arif, Abhishek Midya, Rinku Rabidas, 2020, J. Circuits Syst. Comput..

Analysis of 2D singularities for mammographic mass classification

Abhishek Midya, Jayasree Chakraborty, Rinku Rabidas, 2017, IET Comput. Vis..

Use of Response Permutation to Measure an Imaging Dataset's Susceptibility to Overfitting by Selected Standard Analysis Pipelines.

M. Gonen, C. Moskowitz, Jayasree Chakraborty, 2024, Academic radiology.