Search Results for author:
Found 18 papers, 1 papers with code
Efficient and generalizable prediction of molecular alterations in multiple cancer cohorts using H&E whole slide images
In this work, models were trained for simultaneous prediction of multiple DNA alterations from H&E images using a multi-task approach.
Large Language Models with Retrieval-Augmented Generation for Zero-Shot Disease Phenotyping
Identifying disease phenotypes from electronic health records (EHRs) is critical for numerous secondary uses.
Development and Validation of a Deep Learning-Based Microsatellite Instability Predictor from Prostate Cancer Whole-Slide Images
As such, prediction of MSI status from hematoxylin and eosin (H&E) stained whole-slide images (WSIs) could identify prostate cancer patients most likely to benefit from confirmatory testing and becoming eligible for immunotherapy.
AI-augmented histopathologic review using image analysis to optimize DNA yield and tumor purity from FFPE slides
Then, a pathologist-defined target yield divided by the predicted DNA yield/slide gives the number of slides to scrape.
Imaging-based histological features are predictive of MET alterations in Non-Small Cell Lung Cancer
The association of individual cell features with MET alterations suggested a predictive model could distinguish MET wild-type from MET amplification or MET exon 14 deletion.
Deep Orthogonal Fusion: Multimodal Prognostic Biomarker Discovery Integrating Radiology, Pathology, Genomic, and Clinical Data
Here, we predict the overall survival (OS) of glioma patients from diverse multimodal data with a Deep Orthogonal Fusion (DOF) model.
Predicting Prostate Cancer-Specific Mortality with A.I.-based Gleason Grading
's C-indices were 0. 87 and 0. 85 for continuous and discrete grading, respectively, compared to 0. 79 (95%CI 0. 71-0. 86) for GG obtained from the reports.
Interpretable Survival Prediction for Colorectal Cancer using Deep Learning
Our approach can be used to explain predictions from a prognostic deep learning model and uncover potentially-novel prognostic features that can be reliably identified by people for future validation studies.
Deep learning-based survival prediction for multiple cancer types using histopathology images
Our analysis demonstrates the potential for this approach to provide prognostic information in multiple cancer types, and even within specific pathologic stages.
Similar Image Search for Histopathology: SMILY
SMILY may be a useful general-purpose tool in the pathologist's arsenal, to improve the efficiency of searching large archives of histopathology images, without the need to develop and implement specific tools for each application.
Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection
OOF is often only detected upon careful review, potentially causing rescanning and workflow delays.
Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration
We demonstrate the utility of ARM in the detection of lymph node metastases in breast cancer and the identification of prostate cancer with a latency that supports real-time workflows.
Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer
For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage.
Detecting Cancer Metastases on Gigapixel Pathology Images
At 8 false positives per image, we detect 92. 4% of the tumors, relative to 82. 7% by the previous best automated approach.
Ranked #2 on
Medical Object Detection
on Barrett’s Esophagus
Large Scale Business Discovery from Street Level Imagery
Precise business store front detection enables accurate geo-location of businesses, and further provides input for business categorization, listing generation, etc.
Ontological Supervision for Fine Grained Classification of Street View Storefronts
Modern search engines receive large numbers of business related, local aware queries.
Kepler Presearch Data Conditioning I - Architecture and Algorithms for Error Correction in Kepler Light Curves
This new PDC version, which utilizes a Bayesian approach for removal of systematics, reliably corrects errors in the light curves while at the same time preserving planet transits and other astrophysically interesting signals.
Instrumentation and Methods for Astrophysics Applications
Kepler Presearch Data Conditioning II - A Bayesian Approach to Systematic Error Correction
These errors, which include discontinuities, outliers, systematic trends and other instrumental signatures, obscure astrophysical signals.
Instrumentation and Methods for Astrophysics Applications
