1.1k citations · 3.1k across the 15 of their papers we have counts for
15 papers
Extracting and analyzing 3D histomorphometric features related to perineural and lymphovascular invasion in prostate cancer
Sarah S. L. Chow, Rui Wang, Robert B. Serafin +10
Diagnostic grading of prostate cancer (PCa) relies on the examination of 2D histology sections. However, the limited sampling of specimens afforded by 2D histopathology, and ambigu…
CohortFinder: an open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models
Fan Fan, Georgia Martinez, Thomas Desilvio +10
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)…
PatchSorter: A High Throughput Deep Learning Digital Pathology Tool for Object Labeling
Cedric Walker, Tasneem Talawalla, Robert Toth +11
The discovery of patterns associated with diagnosis, prognosis, and therapy response in digital pathology images often requires intractable labeling of large quantities of histolog…
Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review
Chuang Zhu, Shengjie Liu, Zekuan Yu +15
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 t…
Novel Radiomic Measurements of Tumor- Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers
Nathaniel Braman, Prateek Prasanna, Kaustav Bera +14
Purpose: Tumor-associated vasculature differs from healthy blood vessels by its chaotic architecture and twistedness, which promotes treatment resistance. Measurable differences in…
Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma
Marwa Ismail, Prateek Prasanna, Kaustav Bera +10
The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressiv…
Quick Annotator: an open-source digital pathology based rapid image annotation tool
Runtian Miao, Robert Toth, Yu Zhou +2
Image based biomarker discovery typically requires an accurate segmentation of histologic structures (e.g., cell nuclei, tubules, epithelial regions) in digital pathology Whole Sli…
A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study
Sarah N Dudgeon, Si Wen, Matthew G Hanna +19
Purpose: In this work, we present a collaboration to create a validation dataset of pathologist annotations for algorithms that process whole slide images (WSIs). We focus on data…
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos +6
Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propell…
Can tumor location on pre-treatment MRI predict likelihood of pseudo-progression versus tumor recurrence in Glioblastoma? A feasibility study
Marwa Ismail, Virginia Hill, Volodymyr Statsevych +9
A significant challenge in Glioblastoma (GBM) management is identifying pseudo-progression (PsP), a benign radiation-induced effect, from tumor recurrence, on routine imaging follo…
MRQy: An Open-Source Tool for Quality Control of MR Imaging Data
Amir Reza Sadri, Andrew Janowczyk, Ren Zou +6
We sought to develop a quantitative tool to quickly determine relative differences in MRI volumes both within and between large MR imaging cohorts (such as available in The Cancer…
Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study
Nathaniel Braman, Mohammed El Adoui, Manasa Vulchi +10
Predicting response to neoadjuvant therapy is a vexing challenge in breast cancer. In this study, we evaluate the ability of deep learning to predict response to HER2-targeted neo-…
Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification
Marc Combalia, Veronica Vilaplana
We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared w…
Domain-adversarial neural networks to address the appearance variability of histopathology images
Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof +2
Preparing and scanning histopathology slides consists of several steps, each with a multitude of parameters. The parameters can vary between pathology labs and within the same lab…
Assessment of algorithms for mitosis detection in breast cancer histopathology images
Mitko Veta, Paul J. van Diest, Stefan M. Willems +26
The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be on…