Search Results for author: Anant Madabhushi

Found 11 papers, 3 papers with code

CohortFinder: an open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models

no code implementations17 Jul 2023 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

Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability.

PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for Object Labeling

no code implementations13 Jul 2023 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

Using >100, 000 objects, we demonstrate a >7x improvement in labels per second over unaided labeling, with minimal impact on labeling accuracy, thus enabling high-throughput labeling of large datasets.

Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review

1 code implementation5 May 2023 Chuang Zhu, ShengJie Liu, Zekuan Yu, Feng Xu, 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

For invasive breast cancer, immunohistochemical (IHC) techniques are often used to detect the expression level of human epidermal growth factor receptor-2 (HER2) in breast tissue to formulate a precise treatment plan.

Image Generation SSIM

Novel Radiomic Measurements of Tumor- Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers

no code implementations5 Oct 2022 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

QuanTAV risk scores were prognostic of recurrence free survival in treatment cohorts chemotherapy for breast cancer (p=0. 002, HR=1. 25, 95% CI 1. 08-1. 44, C-index=. 66) and chemoradiation for NSCLC (p=0. 039, HR=1. 28, 95% CI 1. 01-1. 62, C-index=0. 66).

Experimental Design

Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma

no code implementations12 Mar 2021 Marwa Ismail, Prateek Prasanna, Kaustav Bera, Volodymyr Statsevych, Virginia Hill, Gagandeep Singh, Sasan Partovi, Niha Beig, Sean McGarry, Peter Laviolette, Manmeet Ahluwalia, Anant Madabhushi, Pallavi Tiwari

Our work is based on the rationale that highly aggressive tumors tend to grow uncontrollably, leading to pronounced biomechanical tissue deformations in the normal parenchyma, which when combined with local morphological differences in the tumor confines on MRI scans, will comprehensively capture tumor field effect.

Survival Prediction

Quick Annotator: an open-source digital pathology based rapid image annotation tool

1 code implementation6 Jan 2021 Runtian Miao, Robert Toth, Yu Zhou, Anant Madabhushi, Andrew Janowczyk

Image based biomarker discovery typically requires an accurate segmentation of histologic structures (e. g., cell nuclei, tubules, epithelial regions) in digital pathology Whole Slide Images (WSI).

whole slide images

A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises

no code implementations2 Aug 2020 S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, Ronald M. Summers

In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues.

Uncertainty Quantification

MRQy: An Open-Source Tool for Quality Control of MR Imaging Data

1 code implementation10 Apr 2020 Amir Reza Sadri, Andrew Janowczyk, Ren Zou, Ruchika Verma, Niha Beig, Jacob Antunes, Anant Madabhushi, Pallavi Tiwari, Satish E. Viswanath

We present MRQy, a new open-source quality control tool to (a) interrogate MRI cohorts for site- or equipment-based differences, and (b) quantify the impact of MRI artifacts on relative image quality; to help determine how to correct for these variations prior to model development.

Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study

no code implementations22 Jan 2020 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

In a retrospective study encompassing DCE-MRI data from a total of 157 HER2+ breast cancer patients from 5 institutions, we developed and validated a deep learning approach for predicting pathological complete response (pCR) to HER2-targeted NAC prior to treatment.

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