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2020 – today
- 2023
[j60]Yufei Zhou
, Can Fahrettin Koyuncu, Cheng Lu, Rainer Grobholz, Ian Katz, Anant Madabhushi, Andrew Janowczyk:
Multi-site cross-organ calibrated deep learning (MuSClD): Automated diagnosis of non-melanoma skin cancer. Medical Image Anal. 84: 102702 (2023)- [i10]Chuang Zhu, Shengjie Liu, Feng Xu, Zekuan Yu, Arpit Aggarwal, Germán Corredor, Anant Madabhushi, Qixun Qu, Hongwei Fan, Fangda Li, Yueheng Li, Xianchao Guan, Yongbing Zhang, Vivek Kumar Singh, Farhan Akram, Md. Mostafa Kamal Sarker, Zhongyue Shi, Mulan Jin:
Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review. CoRR abs/2305.03546 (2023) - [i9]Cedric Walker, Tasneem Talawalla, Robert Toth, Akhil Ambekar, Kien Rea, Oswin Chamian, Fan Fan, Sabina Berezowska, Sven Rottenberg, Anant Madabhushi, Marie Maillard, Laura Barisoni, Hugo Mark Horlings, Andrew Janowczyk:
PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for Object Labeling. CoRR abs/2307.07528 (2023) - [i8]Fan Fan, Georgia Martinez, Thomas DeSilvio, John Shin, Yijiang Chen, Bangchen Wang, Takaya Ozeki, Maxime W. Lafarge, Viktor H. Koelzer, Laura Barisoni, Anant Madabhushi, Satish E. Viswanath, Andrew Janowczyk:
CohortFinder: an open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models. CoRR abs/2307.08673 (2023) - 2022
- [j59]Jiawei Xie, Xiaohong Pu, Jian He, Yudong Qiu, Cheng Lu, Wei Gao, Xiangxue Wang, Haoda Lu, Jiong Shi, Yuemei Xu, Anant Madabhushi, Xiangshan Fan, Jun Chen, Jun Xu:
Survival prediction on intrahepatic cholangiocarcinoma with histomorphological analysis on the whole slide images. Comput. Biol. Medicine 146: 105520 (2022) - [j58]Jacob T. Antunes, Marwa Ismail, Imran Hossain, Zhoumengdi Wang, Prateek Prasanna, Anant Madabhushi, Pallavi Tiwari, Satish E. Viswanath:
RADIomic Spatial TexturAl Descriptor (RADISTAT): Quantifying Spatial Organization of Imaging Heterogeneity Associated With Tumor Response to Treatment. IEEE J. Biomed. Health Informatics 26(6): 2627-2636 (2022) - [j57]Marwa Ismail, Prateek Prasanna, Kaustav Bera, Volodymyr Statsevych, Virginia B. Hill, Gagandeep Singh, Sasan Partovi, Niha G. Beig, Sean D. McGarry, Peter S. LaViolette, Manmeet Ahluwalia, Anant Madabhushi, Pallavi Tiwari:
Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma. IEEE Trans. Medical Imaging 41(7): 1764-1777 (2022) - [i7]Nathaniel Braman, Prateek Prasanna, Kaustav Bera, Mehdi Alilou, Mohammadhadi Khorrami, Patrick Leo, Maryam Etesami, Manasa Vulchi, Paulette Turk, Amit Gupta, Prantesh Jain, Pingfu Fu, Nathan Pennell, Vamsidhar Velcheti, Jame Abraham, Donna Plecha, Anant Madabhushi:
Novel Radiomic Measurements of Tumor- Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers. CoRR abs/2210.02273 (2022) - 2021
- [j56]Thomas Atta-Fosu, Michael LaBarbera, Soumya Ghose, Paul Schoenhagen, Walid Saliba, Patrick J. Tchou, Bruce D. Lindsay, Milind Y. Desai, Deborah Kwon, Mina K. Chung, Anant Madabhushi:
A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT. BMC Medical Imaging 21(1): 45 (2021) - [j55]Cheng Lu, Can Fahrettin Koyuncu, Germán Corredor, Prateek Prasanna, Patrick Leo, Xiangxue Wang, Andrew Janowczyk, Kaustav Bera, James Lewis, Vamsidhar Velcheti, Anant Madabhushi:
Feature-driven local cell graph (FLocK): New computational pathology-based descriptors for prognosis of lung cancer and HPV status of oropharyngeal cancers. Medical Image Anal. 68: 101903 (2021) - [j54]Jun Xu, Haoda Lu, Haixin Li, Chaoyang Yan, Xiangxue Wang, Min Zang, Dirk G. de Rooij, Anant Madabhushi, Eugene Yujun Xu:
Computerized spermatogenesis staging (CSS) of mouse testis sections via quantitative histomorphological analysis. Medical Image Anal. 70: 101835 (2021) - [j53]Anant Madabhushi, Constantino Carlos Reyes-Aldasoro:
Special issue on computational pathology: An overview. Medical Image Anal. 73: 102151 (2021) - [j52]S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, Ronald M. Summers:
A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises. Proc. IEEE 109(5): 820-838 (2021) - [j51]Azam Moosavi, Natalia Figueiredo, Prateek Prasanna, Sunil K. Srivastava, Sumit Sharma, Anant Madabhushi, Justis P. Ehlers:
Imaging Features of Vessels and Leakage Patterns Predict Extended Interval Aflibercept Dosing Using Ultra-Widefield Angiography in Retinal Vascular Disease: Findings From the PERMEATE Study. IEEE Trans. Biomed. Eng. 68(6): 1777-1786 (2021) - [j50]Amogh Hiremath, Kaustav Bera, Lei Yuan, Pranjal Vaidya, Mehdi Alilou, Jennifer Furin, Keith Armitage, Robert Gilkeson, Mengyao Ji, Pingfu Fu, Amit Gupta, Cheng Lu, Anant Madabhushi:
Integrated Clinical and CT Based Artificial Intelligence Nomogram for Predicting Severity and Need for Ventilator Support in COVID-19 Patients: A Multi-Site Study. IEEE J. Biomed. Health Informatics 25(11): 4110-4118 (2021) - [c161]Amogh Hiremath, Lei Yuan, Rakesh Shiradkar, Kaustav Bera, Vidya Sankar Viswanathan, Pranjal Vaidya, Jennifer Furin, Keith Armitage, Robert Gilkeson, Mengyao Ji, Pingfu Fu, Amit Gupta, Cheng Lu, Anant Madabhushi:
LuMiRa: An Integrated Lung Deformation Atlas and 3D-CNN Model of Infiltrates for COVID-19 Prognosis. MICCAI (7) 2021: 367-377 - [c160]Amir Reza Sadri, Sepideh Azarianpour Esfahani, Prathyush Chirra, Jacob Antunes, Pavithran Pattiam Giriprakash, Patrick Leo, Anant Madabhushi, Satish E. Viswanath:
SPARTA: An Integrated Stability, Discriminability, and Sparsity Based Radiomic Feature Selection Approach. MICCAI (3) 2021: 445-455 - [e11]Tanveer F. Syeda-Mahmood, Xiang Li, Anant Madabhushi, Hayit Greenspan, Quanzheng Li, Richard M. Leahy, Bin Dong, Hongzhi Wang:
Multimodal Learning for Clinical Decision Support - 11th International Workshop, ML-CDS 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings. Lecture Notes in Computer Science 13050, Springer 2021, ISBN 978-3-030-89846-5 [contents] - [i6]Marwa Ismail, Prateek Prasanna, Kaustav Bera, Volodymyr Statsevych, Virginia B. Hill, Gagandeep Singh, Sasan Partovi, Niha G. Beig, Sean D. McGarry, Peter S. LaViolette, Manmeet Ahluwalia, Anant Madabhushi, Pallavi Tiwari:
Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma. CoRR abs/2103.07423 (2021) - 2020
- [j49]Chaoyang Yan, Kazuaki Nakane, Xiangxue Wang, Yao Fu, Haoda Lu, Xiangshan Fan, Michael D. Feldman, Anant Madabhushi, Jun Xu:
Automated gleason grading on prostate biopsy slides by statistical representations of homology profile. Comput. Methods Programs Biomed. 194: 105528 (2020) - [j48]Marwa Ismail, Virginia B. Hill, Volodymyr Statsevych, Evan Mason, Ramon Correa, Prateek Prasanna, Gagandeep Singh, Kaustav Bera, Rajat Thawani, Manmeet Ahluwalia, Anant Madabhushi, Pallavi Tiwari:
Can Tumor Location on Pre-treatment MRI Predict Likelihood of Pseudo-Progression vs. Tumor Recurrence in Glioblastoma? - A Feasibility Study. Frontiers Comput. Neurosci. 14: 563439 (2020) - [c159]Sepideh Azarianpour, Germán Corredor, Kaustav Bera, Patrick Leo, Nathaniel Braman, Pingfu Fu, Haider Mahdi, Anant Madabhushi:
Computer extracted features related to the spatial arrangement of tumor-infiltrating lymphocytes predict overall survival in epithelial ovarian cancer receiving adjuvant chemotherapy. Medical Imaging: Digital Pathology 2020: 113200Q - [c158]Can Fahrettin Koyuncu, Andrew Janowczyk, Cheng Lu, Patrick Leo, Mehdi Alilou, Adam K. Glaser, Nicholas P. Reder, Jonathan T. C. Liu, Anant Madabhushi:
Three-dimensional histo-morphometric features from light sheet microscopy images result in improved discrimination of benign from malignant glands in prostate cancer. Medical Imaging: Digital Pathology 2020: 113200G - [c157]Shayan Monabbati, Patrick Leo, Kaustav Bera, Behtash G. Nezami, Claire W. Michael, Aparna Harbhajanka, Anant Madabhushi:
Texture features distinguish benign cell clusters from adenocarcinomas on bile duct brushing cytology images. Medical Imaging: Digital Pathology 2020: 113200I - [c156]Ruiwen Ding, Prateek Prasanna, Germán Corredor, Cheng Lu, Priya Velu, Khoi Le, Patrick Leo, Niha G. Beig, Vamsidhar Velcheti, David L. Rimm, Kurt A. Schalper, Anant Madabhushi:
Compactness measures of tumor infiltrating lymphocytes in lung adenocarcinoma are associated with overall patient survival and immune scores. Medical Imaging: Digital Pathology 2020: 1132003 - [c155]Sara ArabYarmohammadi, Zelin Zhang, Patrick Leo, Marjan Firouznia, Andrew Janowczyk, Haojia Li, Nathaniel M. Braman, Kaustav Bera, Behtash G. Nezami, Howard Meyerson, Jun Xu, Leland Metheny, Anant Madabhushi:
Computationally derived cytological image markers for predicting risk of relapse in acute myeloid leukemia patients following bone marrow transplantation. Medical Imaging: Digital Pathology 2020: 1132004 - [e10]Tanveer F. Syeda-Mahmood, Klaus Drechsler, Hayit Greenspan, Anant Madabhushi, Alexandros Karargyris, Marius George Linguraru, Cristina Oyarzun Laura, Raj Shekhar, Stefan Wesarg, Miguel Ángel González Ballester, Marius Erdt:
Multimodal Learning for Clinical Decision Support and Clinical Image-Based Procedures - 10th International Workshop, ML-CDS 2020, and 9th International Workshop, CLIP 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings. Lecture Notes in Computer Science 12445, Springer 2020, ISBN 978-3-030-60945-0 [contents] - [i5]Nathaniel Braman, Mohammed El Adoui, Manasa Vulchi, Paulette Turk, Maryam Etesami, Pingfu Fu, Kaustav Bera, Stylianos Drisis, Vinay Varadan, Donna Plecha, Mohammed Benjelloun, Jame Abraham, Anant Madabhushi:
Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study. CoRR abs/2001.08570 (2020) - [i4]Amir Reza Sadri, Andrew Janowczyk, Ren Zou, Ruchika Verma, Jacob Antunes, Anant Madabhushi, Pallavi Tiwari, Satish E. Viswanath:
MRQy: An Open-Source Tool for Quality Control of MR Imaging Data. CoRR abs/2004.04871 (2020) - [i3]Marwa Ismail, Virginia B. Hill, Volodymyr Statsevych, Evan Mason, Ramon Correa, Prateek Prasanna, Gagandeep Singh, Kaustav Bera, Rajat Thawani, Anant Madabhushi, Manmeet Ahluwalia, Pallavi Tiwari:
Can tumor location on pre-treatment MRI predict likelihood of pseudo-progression versus tumor recurrence in Glioblastoma? A feasibility study. CoRR abs/2006.09483 (2020) - [i2]S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, Ronald M. Summers:
A review of deep learning in medical imaging: Image traits, technology trends, case studies with progress highlights, and future promises. CoRR abs/2008.09104 (2020)
2010 – 2019
- 2019
- [j47]Satish Viswanath, Prathyush Chirra, Michael Yim, Neil M. Rofsky, Andrei Purysko, Mark A. Rosen, B. Nicolas Bloch, Anant Madabhushi:
Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study. BMC Medical Imaging 19(1): 22:1-22:12 (2019) - [c154]Jun Xu, Chengfei Cai, Yangshu Zhou, Bo Yao, Geyang Xu, Xiangxue Wang, Ke Zhao, Anant Madabhushi, Zaiyi Liu, Li Liang:
Multi-tissue Partitioning for Whole Slide Images of Colorectal Cancer Histopathology Images with Deeptissue Net. ECDP 2019: 100-108 - [c153]Jun Xu, Haoda Lu, Haixin Li, Xiangxue Wang, Anant Madabhushi, Yujun Xu:
Histopathological Image Analysis on Mouse Testes for Automated Staging of Mouse Seminiferous Tubule. ECDP 2019: 117-124 - [c152]Prateek Prasanna, Justis Ehlers, Vishal Bobba, Natalia Figueredo, Cheng Lu, Sumit Sharma, Sunil Srivastava, Anant Madabhushi:
Spatial arrangement of leakage patterns in diabetic macular edema is associated with tolerance of aflibercept treatment interval length: preliminary findings. Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging 2019: 1095311 - [c151]Prateek Prasanna, Justis Ehlers, Nathaniel Braman, Natalia Figueredo, Vishal Bobba, Sumit Sharma, Sunil Srivastava, Anant Madabhushi:
Morphology of vascular network in eyes with diabetic macular edema varies based on tolerance of aflibercept treatment interval length: preliminary findings. Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging 2019: 1095312 - [c150]Mohammadhadi Khorrami, Mehdi Alilou, Prateek Prasanna, Pradnya Patil, Pirya Velu, Kaustav Bera, Pingfu Fu, Vamsidhar Velcheti, Anant Madabhushi:
A combination of intra- and peritumoral features on baseline CT scans is associated with overall survival in non-small cell lung cancer patients treated with immune checkpoint inhibitors: a multi-agent multi-site study. Medical Imaging: Computer-Aided Diagnosis 2019: 109500R - [c149]Ruchika Verma, Ramon Correa, Virginia B. Hill, Niha G. Beig, Abdelkader Mahammedi, Anant Madabhushi, Pallavi Tiwari:
Radiomics of the lesion habitat on pre-treatment MRI predicts response to chemo-radiation therapy in Glioblastoma. Medical Imaging: Computer-Aided Diagnosis 2019: 109500B - [c148]Mehdi Alilou, Pranjal Vaidya, Mohammadhadi Khorrami, Alexia Zagouras, Pradnya Patil, Kaustav Bera, Pingfu Fu, Vamsidhar Velcheti, Anant Madabhushi:
Quantitative vessel tortuosity radiomics on baseline non-contrast lung CT predict response to immunotherapy and are prognostic of overall survival. Medical Imaging: Computer-Aided Diagnosis 2019: 109501F - [c147]Niha G. Beig, Prateek Prasanna, Virginia B. Hill, Ruchika Verma, Vinay Varadan, Anant Madabhushi, Pallavi Tiwari:
Radiogenomic characterization of response to chemo-radiation therapy in glioblastoma is associated with PI3K/AKT/mTOR and apoptosis signaling pathways. Medical Imaging: Computer-Aided Diagnosis 2019: 109501B - [c146]Sukanya Iyer, Marwa Ismail, Benita Tamrazi, Ashley Margol, Ruchika Verma, Ramon Correa, Prateek Prasanna, Niha G. Beig, Kaustav Bera, Volodymyr Statsevych, Alexander R. Judkins, Anant Madabhushi, Pallavi Tiwari:
Deformation heterogeneity radiomics to predict molecular subtypes of pediatric Medulloblastoma on routine MRI. Medical Imaging: Computer-Aided Diagnosis 2019: 109501E - [c145]Lin Li, Rakesh Shiradkar, Ahmad Algohary, Patrick Leo, Cristina Magi-Galluzzi, Eric Klein, Andrei Purysko, Anant Madabhushi:
Radiomic features derived from pre-operative multi-parametric MRI of prostate cancer are associated with Decipher risk score. Medical Imaging: Computer-Aided Diagnosis 2019: 109503Y - [c144]Jeffrey E. Eben, Nathaniel Braman, Anant Madabhushi:
Response Estimation Through Spatially Oriented Neural Network and Texture Ensemble (RESONATE). MICCAI (4) 2019: 602-610 - [c143]Jacob Antunes, Zhouping Wei, Charlems Álvarez Jimenez, Eduardo Romero, Marwa Ismail, Anant Madabhushi, Pallavi Tiwari, Satish E. Viswanath:
STructural Rectal Atlas Deformation (StRAD) Features for Characterizing Intra- and Peri-wall Chemoradiation Response on MRI. MICCAI (4) 2019: 611-619 - [c142]Cristian Barrera, Germán Corredor, Xiangxue Wang, Kurt A. Schalper, David L. Rimm, Vamsidhar Velcheti, Anant Madabhushi, Edgar Eduardo Romero Castro:
Phenotyping tumor infiltrating lymphocytes (PhenoTIL) on H&E tissue images: predicting recurrence in lung cancer. Medical Imaging: Digital Pathology 2019: 1095607 - [c141]Siddhartha Nanda, Jacob T. Antunes, Amrish Selvam, Kaustav Bera, Justin T. Brady, Jayakrishna Gollamudi, Kenneth Friedman, Joseph E. Willis, Conor P. Delaney, Raj M. Paspulati, Anant Madabhushi, Satish E. Viswanath:
Integrating radiomic features from T2-weighted and contrast-enhanced MRI to evaluate pathologic rectal tumor regression after chemoradiation. Medical Imaging: Image-Guided Procedures 2019: 109512R - [c140]Michael C. Yim, Zhouping Wei, Jacob Antunes, Neil K. R. Sehgal, Kaustav Bera, Justin T. Brady, Kenneth Friedman, Joseph E. Willis, Andrei Purysko, Raj M. Paspulati, Anant Madabhushi, Satish E. Viswanath:
Radiomic characterization of perirectal fat on MRI enables accurate assessment of tumor regression and lymph node metastasis in rectal cancers after chemoradiation. Medical Imaging: Image-Guided Procedures 2019: 109512A - [e9]Kenji Suzuki, Mauricio Reyes, Tanveer F. Syeda-Mahmood, Ben Glocker, Roland Wiest, Yaniv Gur, Hayit Greenspan, Anant Madabhushi:
Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support - Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings. Lecture Notes in Computer Science 11797, Springer 2019, ISBN 978-3-030-33849-7 [contents] - 2018
- [j46]Andrew Janowczyk, Scott Doyle, Hannah Gilmore, Anant Madabhushi:
A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images. Comput. methods Biomech. Biomed. Eng. Imaging Vis. 6(3): 270-276 (2018) - [c139]Prathyush Chirra, Patrick Leo, Michael Yim, B. Nicolas Bloch, Ardeshir R. Rastinehad, Andrei Purysko, Mark Rosen, Anant Madabhushi, Satish Viswanath:
Empirical evaluation of cross-site reproducibility in radiomic features for characterizing prostate MRI. Medical Imaging: Computer-Aided Diagnosis 2018: 105750B - [c138]Kavya Ravichandran, Nathaniel Braman, Andrew Janowczyk, Anant Madabhushi:
A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI. Medical Imaging: Computer-Aided Diagnosis 2018: 105750C - [c137]Cheng Lu, Xiangxue Wang, Prateek Prasanna, Germán Corredor, Geoffrey Sedor, Kaustav Bera, Vamsidhar Velcheti, Anant Madabhushi:
Feature Driven Local Cell Graph (FeDeG): Predicting Overall Survival in Early Stage Lung Cancer. MICCAI (2) 2018: 407-416 - [c136]Nathaniel Braman, Prateek Prasanna, Mehdi Alilou, Niha G. Beig, Anant Madabhushi:
Vascular Network Organization via Hough Transform (VaNgOGH): A Novel Radiomic Biomarker for Diagnosis and Treatment Response. MICCAI (2) 2018: 803-811 - [c135]Germán Corredor, Xiangxue Wang, Cheng Lu, Vamsidhar Velcheti, Eduardo Romero, Anant Madabhushi:
A watershed and feature-based approach for automated detection of lymphocytes on lung cancer images. Medical Imaging: Digital Pathology 2018: 105810R - [c134]Patrick Leo, Eswar Shankar, Robin Elliott, Andrew Janowczyk, Anant Madabhushi, Sanjay Gupta:
Combination of nuclear NF-κB/p65 localization and gland morphological features from surgical specimens appears to be predictive of early biochemical recurrence in prostate cancer patients. Medical Imaging: Digital Pathology 2018: 105810D - [c133]Pranjal Vaidya, Xiangxue Wang, Kaustav Bera, Arjun Khunger, Humberto Choi, Pradnya Patil, Vamsidhar Velcheti, Anant Madabhushi:
RaPtomics: integrating radiomic and pathomic features for predicting recurrence in early stage lung cancer. Medical Imaging: Digital Pathology 2018: 105810M - [e8]Danail Stoyanov, Zeike Taylor, Gustavo Carneiro, Tanveer F. Syeda-Mahmood, Anne L. Martel, Lena Maier-Hein, João Manuel R. S. Tavares, Andrew P. Bradley, João Paulo Papa, Vasileios Belagiannis, Jacinto C. Nascimento, Zhi Lu, Sailesh Conjeti, Mehdi Moradi, Hayit Greenspan, Anant Madabhushi:
Deep Learning in Medical Image Analysis - and - Multimodal Learning for Clinical Decision Support - 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings. Lecture Notes in Computer Science 11045, Springer 2018, ISBN 978-3-030-00888-8 [contents] - 2017
- [j45]Satish Viswanath, Pallavi Tiwari, George Lee, Anant Madabhushi:
Dimensionality reduction-based fusion approaches for imaging and non-imaging biomedical data: concepts, workflow, and use-cases. BMC Medical Imaging 17(1): 2:1-2:17 (2017) - [j44]Andrew Janowczyk, Ajay Basavanhally, Anant Madabhushi:
Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology. Comput. Medical Imaging Graph. 57: 50-61 (2017) - [c132]Niha G. Beig, Jay Patel, Prateek Prasanna, Sasan Partovi, Vinay Varadan, Anant Madabhushi, Pallavi Tiwari:
Radiogenomic analysis of hypoxia pathway reveals computerized MRI descriptors predictive of overall survival in glioblastoma. Medical Imaging: Computer-Aided Diagnosis 2017: 101341U - [c131]Soumya Ghose, Rakesh Shiradkar, Mirabela Rusu, Jhimli Mitra, Rajat Thawani, Michael D. Feldman, Amar Gupta, Andrei Purysko, Lee Ponsky, Anant Madabhushi:
Field Effect Induced Organ Distension (FOrge) Features Predicting Biochemical Recurrence from Pre-treatment Prostate MRI. MICCAI (2) 2017: 442-449 - [c130]Prateek Prasanna, Jhimli Mitra, Niha G. Beig, Sasan Partovi, Gagandeep Singh, Marco Pinho, Anant Madabhushi, Pallavi Tiwari:
Radiographic-Deformation and Textural Heterogeneity (r-DepTH): An Integrated Descriptor for Brain Tumor Prognosis. MICCAI (2) 2017: 459-467 - [c129]Jacob Antunes, Prateek Prasanna, Anant Madabhushi, Pallavi Tiwari, Satish Viswanath:
RADIomic Spatial TexturAl descripTor (RADISTAT): Characterizing Intra-tumoral Heterogeneity for Response and Outcome Prediction. MICCAI (2) 2017: 468-476 - [c128]Mehdi Alilou, Mahdi Orooji, Anant Madabhushi:
Intra-perinodular Textural Transition (Ipris): A 3D Descriptor for Nodule Diagnosis on Lung CT. MICCAI (3) 2017: 647-655 - [c127]Paula Toro, Germán Corredor, Xiangxue Wang, Viviana Arias, Vamsidhar Velcheti, Anant Madabhushi, Eduardo Romero:
Quantifying expert diagnosis variability when grading tumor-infiltrating lymphocytes. SIPAIM 2017: 1057202 - [c126]Juan D. García-Arteaga, Germán Corredor, Xiangxue Wang, Vamsidhar Velcheti, Anant Madabhushi, Eduardo Romero:
A lymphocyte spatial distribution graph-based method for automated classification of recurrence risk on lung cancer images. SIPAIM 2017: 1057203 - [p1]Jeffrey J. Nirschl, Andrew Janowczyk, Eliot G. Peyster, Renee Frank, Kenneth B. Margulies, Michael D. Feldman, Anant Madabhushi:
Deep Learning Tissue Segmentation in Cardiac Histopathology Images. Deep Learning for Medical Image Analysis 2017: 179-195 - [e7]M. Jorge Cardoso, Tal Arbel, Gustavo Carneiro, Tanveer F. Syeda-Mahmood, João Manuel R. S. Tavares, Mehdi Moradi, Andrew P. Bradley, Hayit Greenspan, João Paulo Papa, Anant Madabhushi, Jacinto C. Nascimento, Jaime S. Cardoso, Vasileios Belagiannis, Zhi Lu:
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, 2017, Proceedings. Lecture Notes in Computer Science 10553, Springer 2017, ISBN 978-3-319-67557-2 [contents] - 2016
- [j43]Jun Xu, Xiaofei Luo, Guanhao Wang, Hannah Gilmore, Anant Madabhushi:
A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images. Neurocomputing 191: 214-223 (2016) - [j42]Anant Madabhushi, George Lee:
Image analysis and machine learning in digital pathology: Challenges and opportunities. Medical Image Anal. 33: 170-175 (2016) - [j41]Shoshana B. Ginsburg, George Lee, Sahirzeeshan Ali, Anant Madabhushi:
Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology. IEEE Trans. Medical Imaging 35(1): 76-88 (2016) - [j40]Jun Xu, Lei Xiang, Qingshan Liu, Hannah Gilmore, Jianzhong Wu, Jinghai Tang, Anant Madabhushi:
Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images. IEEE Trans. Medical Imaging 35(1): 119-130 (2016) - [c125]Patrick Leo, George Lee, Anant Madabhushi:
Evaluating stability of histomorphometric features across scanner and staining variations: predicting biochemical recurrence from prostate cancer whole slide images. Medical Imaging: Digital Pathology 2016: 97910I - [c124]David Romo-Bucheli, Andrew Janowczyk, Eduardo Romero, Hannah Gilmore, Anant Madabhushi:
Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images. Medical Imaging: Digital Pathology 2016: 979106 - [c123]Lin Li, Mirabela Rusu, Satish Viswanath, Gregory Penzias, Shivani Pahwa, Jay Gollamudi, Anant Madabhushi:
Multi-modality registration via multi-scale textural and spectral embedding representations. Medical Imaging: Image Processing 2016: 978446 - [e6]Metin N. Gurcan, Anant Madabhushi:
Medical Imaging 2016: Digital Pathology, San Diego, California, United States, 27 February - 3 March 2016. SPIE Proceedings 9791, SPIE 2016, ISBN 9781510600263 [contents] - 2015
- [j39]Sahirzeeshan Ali, Robert Veltri, Jonathan I. Epstein, Christhunesa Christudass, Anant Madabhushi:
Selective invocation of shape priors for deformable segmentation and morphologic classification of prostate cancer tissue microarrays. Comput. Medical Imaging Graph. 41: 3-13 (2015) - [j38]Jun Xu, Lei Xiang, Guanhao Wang, Shridar Ganesan, Michael D. Feldman, Natalie N. C. Shih, Hannah Gilmore, Anant Madabhushi:
Sparse Non-negative Matrix Factorization (SNMF) based color unmixing for breast histopathological image analysis. Comput. Medical Imaging Graph. 46: 20-29 (2015) - [j37]Mitko Veta, Paul J. van Diest, Stefan M. Willems, Haibo Wang, Anant Madabhushi, Angel Cruz-Roa, Fabio A. González, Anders Boesen Lindbo Larsen, Jacob S. Vestergaard, Anders B. Dahl, Dan C. Ciresan, Jürgen Schmidhuber, Alessandro Giusti, Luca Maria Gambardella, F. Boray Tek, Thomas Walter, Ching-Wei Wang, Satoshi Kondo, Bogdan J. Matuszewski, Frédéric Precioso, Violet Snell, Josef Kittler, Teófilo Emídio de Campos, Adnan Mujahid Khan, Nasir M. Rajpoot, Evdokia Arkoumani, Miangela M. Lacle, Max A. Viergever, Josien P. W. Pluim:
Assessment of algorithms for mitosis detection in breast cancer histopathology images. Medical Image Anal. 20(1): 237-248 (2015) - [j36]George Lee, Asha Singanamalli, Haibo Wang, Michael D. Feldman, Stephen R. Master, Natalie N. C. Shih, Elaine Spangler, Timothy R. Rebbeck, John Tomaszewski, Anant Madabhushi:
Supervised Multi-View Canonical Correlation Analysis (sMVCCA): Integrating Histologic and Proteomic Features for Predicting Recurrent Prostate Cancer. IEEE Trans. Medical Imaging 34(1): 284-297 (2015) - [c122]Sebastian Otálora, Angel Cruz-Roa, John Edison Arevalo Ovalle, Manfredo Atzori, Anant Madabhushi, Alexander R. Judkins, Fabio A. González, Henning Müller, Adrien Depeursinge:
Combining Unsupervised Feature Learning and Riesz Wavelets for Histopathology Image Representation: Application to Identifying Anaplastic Medulloblastoma. MICCAI (1) 2015: 581-588 - [e5]Metin N. Gurcan, Anant Madabhushi:
Medical Imaging 2015: Digital Pathology, Orlando, Florida, United States, 21-26 February 2015. SPIE Proceedings 9420, SPIE 2015, ISBN 9781628415100 [contents] - 2014
- [j35]Robert Toth, Bryan J. Traughber, Rodney J. Ellis, John Kurhanewicz, Anant Madabhushi:
A domain constrained deformable (DoCD) model for co-registration of pre- and post-radiated prostate MRI. Neurocomputing 144: 3-12 (2014) - [j34]Satish Viswanath, Robert Toth, Mirabela Rusu, Dan Sperling, Herbert Lepor, Jurgen J. Fütterer, Anant Madabhushi:
Identifying quantitative in vivo multi-parametric MRI features for treatment related changes after laser interstitial thermal therapy of prostate cancer. Neurocomputing 144: 13-23 (2014) - [j33]Tao Wan, B. Nicolas Bloch, Shabbar Danish, Anant Madabhushi:
A learning based fiducial-driven registration scheme for evaluating laser ablation changes in neurological disorders. Neurocomputing 144: 24-37 (2014) - [j32]Geert Litjens, Robert Toth, Wendy J. M. van de Ven, Caroline Hoeks, Sjoerd Kerkstra, Bram van Ginneken, Graham Vincent, Gwenaël Guillard, Neil Birbeck, Jindang Zhang, Robin Strand, Filip Malmberg, Yangming Ou, Christos Davatzikos, Matthias Kirschner, Florian Jung, Jing Yuan, Wu Qiu, Qinquan Gao, Philip J. Edwards, Bianca Maan, Ferdinand van der Heijden, Soumya Ghose, Jhimli Mitra, Jason Dowling, Dean C. Barratt, Henkjan J. Huisman, Anant Madabhushi:
Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge. Medical Image Anal. 18(2): 359-373 (2014) - [c121]Pallavi Tiwari, Prateek Prasanna, Lisa Rogers, Leo Wolansky, Chaitra Badve, Andrew Sloan, Mark Cohen, Anant Madabhushi:
Texture descriptors to distinguish radiation necrosis from recurrent brain tumors on multi-parametric MRI. Medical Imaging: Computer-Aided Diagnosis 2014: 90352B - [c120]Shoshana B. Ginsburg, Mirabela Rusu, John Kurhanewicz, Anant Madabhushi:
Computer extracted texture features on T2w MRI to predict biochemical recurrence following radiation therapy for prostate cancer. Medical Imaging: Computer-Aided Diagnosis 2014: 903509 - [c119]Geert J. S. Litjens, R. Elliott, Natalie Shih, Michael D. Feldman, Jelle O. Barentsz, Christina A. Hulsbergen van de Kaa, Iringo Kovacs, Henkjan J. Huisman, Anant Madabhushi:
Distinguishing prostate cancer from benign confounders via a cascaded classifier on multi-parametric MRI. Medical Imaging: Computer-Aided Diagnosis 2014: 903512 - [c118]Mirabela Rusu, John Kurhanewicz, Ashutosh Tewari, Anant Madabhushi:
A prostate MRI atlas of biochemical failures following cancer treatment. Medical Imaging: Computer-Aided Diagnosis 2014: 903513 - [c117]Prateek Prasanna, Pallavi Tiwari, Anant Madabhushi:
Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): Distinguishing Tumor Confounders and Molecular Subtypes on MRI. MICCAI (3) 2014: 73-80 - [c116]Haibo Wang, Asha Singanamalli, Shoshana Ginsburg, Anant Madabhushi:
Selecting Features with Group-Sparse Nonnegative Supervised Canonical Correlation Analysis: Multimodal Prostate Cancer Prognosis. MICCAI (3) 2014: 385-392 - [c115]Haibo Wang, Angel Cruz-Roa, Ajay Basavanhally, Hannah Gilmore, Natalie Shih, Mike Feldman, John Tomaszewski, Fabio A. González, Anant Madabhushi:
Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection. Medical Imaging: Digital Pathology 2014: 90410B - [c114]Angel Cruz-Roa, Ajay Basavanhally, Fabio A. González, Hannah Gilmore, Michael D. Feldman, Shridar Ganesan, Natalie Shih, John Tomaszewski, Anant Madabhushi:
Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. Medical Imaging: Digital Pathology 2014: 904103 - [c113]Geert Litjens, Henkjan J. Huisman, R. Elliott, Natalie Shih, Michael D. Feldman, Satish Viswanath, Jurgen J. Fütterer, J. Bomers, Anant Madabhushi:
Distinguishing benign confounding treatment changes from residual prostate cancer on MRI following laser ablation. Medical Imaging: Image-Guided Procedures 2014: 90361D - [c112]