J. W. Gray

Towards Generalizability and Robustness in Biological Object Detection in Electron Microscopy Images

This study improves model generalizability in biological object detection using electron microscopy images through normalization and augmentation techniques, achieving a 29% average precision boost.

Efficient semi-supervised semantic segmentation of electron microscopy cancer images with sparse annotations

The paper compares deep learning models for segmenting nuclei and nucleoli in cancer EM images using sparse labels and semi-supervised learning, showing improvements in 3D Dice scores.

Longitudinal and multimodal auditing of tumor adaptation to CDK4/6 inhibitors in HR+ metastatic breast cancers

This study creates a multimodal tumor atlas to analyze how metastatic HR+ breast cancers adapt to CDK4/6 inhibitors, revealing mechanisms like mTORC1 activation and immune microenvironment changes.

A Novel Mouse Model that Recapitulates the Heterogeneity of Human Triple Negative Breast Cancer

This paper develops a novel mouse model of TNBC by combining MYC amplification and PTEN deletion, showing it recapitulates human TNBC heterogeneity through omic analyses.

A framework for multiplex imaging optimization and reproducible analysis

The paper introduces a framework and software for optimizing and reproducibly analyzing multiplex imaging data, focusing on cyclic immunofluorescence.

Multiplex spatial systems analysis of local nanodose drug responses predicts effective treatment combinations of immunotherapies and targeted agents in mammary carcinoma

The paper develops a MIMA system to assess drug combinations in tumors, identifying effective immunotherapy and targeted agent combinations with minimal toxicity.

Robust Segmentation of Cellular Ultrastructure on Sparsely Labeled 3D Electron Microscopy Images using Deep Learning

This paper develops a deep learning method to segment cellular ultrastructures in 3D EM images, achieving high accuracy for nuclei and nucleoli in cancer biopsies.

Noise Reconstruction & Removal Network: A New Architecture to Denoise FIB-SEM Images

The paper introduces an unsupervised Noise Reconstruction and Removal Network for denoising FIB-SEM images, achieving results comparable to or better than supervised methods.

Transfer Learning for Inference of Metastatic Origin from Whole Slide Histology

This paper uses transfer learning to predict metastatic origin from H&E-stained tumor images, achieving higher accuracy than models trained on primary or metastatic tumors alone.

Relevance of Circulating Hybrid Cells as a Non-Invasive Biomarker for Myriad Solid Tumors

This study identifies circulating hybrid cells (CHCs) as a novel biomarker for metastatic solid tumors, showing they outnumber CTCs and exhibit tumor-initiating properties across multiple cancer types.

An Omic and Multidimensional Spatial Atlas from Serial Biopsies of an Evolving Metastatic Breast Cancer

This paper presents an OMS Atlas from serial biopsies of a metastatic breast cancer patient, linking clinical data to multi-omic and spatial profiles to study therapy-associated heterogeneity and resistance mechanisms.

Predicting Primary Site of Secondary Liver Cancer with a Neural Estimator of Metastatic Origin (NEMO)

The paper introduces a deep learning model to predict the metastatic origin of secondary liver cancer from H&E stained tissue sections, achieving 90.2% accuracy comparable to expert pathologists.

A workflow for visualizing human cancer biopsies using large-format electron microscopy

This paper describes a workflow for 2D and 3D imaging of human cancer biopsies using electron microscopy to reveal ultrastructural details not visible with conventional 2D EM.

High-throughput single-particle tracking reveals nested membrane nanodomain organization that dictates Ras diffusion and trafficking

This study uses single-particle tracking to show that KRas diffusion and trafficking are regulated by nested membrane nanodomains in U2OS cells.

A multi-center study on factors influencing the reproducibility of in vitro drug-response studies

This study investigates factors affecting the reproducibility of in vitro drug-response assays across multiple centers, identifying context-sensitive factors that impact results.