55 citations · 158 across the 26 of their papers we have counts for
28 papers
BigCell: Generating Gigapixel Whole-Slide Images
Srikar Yellapragada, Alexandros Graikos, Zilinghan Li +10
Whole-slide images (WSIs) are gigapixel-scale digital scans of histopathology tissue and serve as the primary basis for cancer diagnosis. Existing histopathology generative models…
Pathologist Attention-Aligned Report Generation for Prostate Histopathology
Ruoyu Xue, Suryakant Singh, Souradeep Chakraborty +12
The allocation of visual attention by pathologists during cancer diagnosis is a highly selective process that critically shapes the information extracted from whole-slide images (W…
Towards interpretable prediction of recurrence risk in breast cancer using pathology foundation models
Jakub R. Kaczmarzyk, Sarah C. Van Alsten, Alyssa J. Cozzo +5
Transcriptomic assays such as the PAM50-based ROR-P score guide recurrence risk stratification in non-metastatic, ER-positive, HER2-negative breast cancer but are not universally a…
Measuring and Predicting Where and When Pathologists Focus their Visual Attention while Grading Whole Slide Images of Cancer
Souradeep Chakraborty, Ruoyu Xue, Rajarsi Gupta +11
The ability to predict the attention of expert pathologists could lead to decision support systems for better pathology training. We developed methods to predict the spatio-tempora…
PixCell: A generative foundation model for digital histopathology images
Srikar Yellapragada, Alexandros Graikos, Zilinghan Li +11
The digitization of histology slides has revolutionized pathology, providing massive datasets for cancer diagnosis and research. Self-supervised and vision-language models have bee…
GECKO: Gigapixel Vision-Concept Contrastive Pretraining in Histopathology
Saarthak Kapse, Pushpak Pati, Srikar Yellapragada +5
Pretraining a Multiple Instance Learning (MIL) aggregator enables the derivation of Whole Slide Image (WSI)-level embeddings from patch-level representations without supervision. W…
ZoomLDM: Latent Diffusion Model for multi-scale image generation
Srikar Yellapragada, Alexandros Graikos, Kostas Triaridis +4
Diffusion models have revolutionized image generation, yet several challenges restrict their application to large-image domains, such as digital pathology and satellite imagery. Gi…
-Brush: Controllable Large Image Synthesis with Diffusion Models in Infinite Dimensions
Minh-Quan Le, Alexandros Graikos, Srikar Yellapragada +3
Synthesizing high-resolution images from intricate, domain-specific information remains a significant challenge in generative modeling, particularly for applications in large-image…
Decoding the visual attention of pathologists to reveal their level of expertise
Souradeep Chakraborty, Dana Perez, Paul Friedman +7
We present a method for classifying the expertise of a pathologist based on how they allocated their attention during a cancer reading. We engage this decoding task by developing a…
SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathology
Saarthak Kapse, Pushpak Pati, Srijan Das +7
Introducing interpretability and reasoning into Multiple Instance Learning (MIL) methods for Whole Slide Image (WSI) analysis is challenging, given the complexity of gigapixel slid…
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning
Saarthak Kapse, Srijan Das, Jingwei Zhang +4
We propose DiRL, a Diversity-inducing Representation Learning technique for histopathology imaging. Self-supervised learning techniques, such as contrastive and non-contrastive app…
Open and reusable deep learning for pathology with WSInfer and QuPath
Jakub R. Kaczmarzyk, Alan O'Callaghan, Fiona Inglis +5
The field of digital pathology has seen a proliferation of deep learning models in recent years. Despite substantial progress, it remains rare for other researchers and pathologist…
Halcyon -- A Pathology Imaging and Feature analysis and Management System
Erich Bremer, Tammy DiPrima, Joseph Balsamo +3
Halcyon is a new pathology imaging analysis and feature management system based on W3C linked-data open standards and is designed to scale to support the needs for the voluminous p…
Topology-Guided Multi-Class Cell Context Generation for Digital Pathology
Shahira Abousamra, Rajarsi Gupta, Tahsin Kurc +3
In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging…
ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis
Xuan Xu, Saarthak Kapse, Rajarsi Gupta +1
Generative AI has received substantial attention in recent years due to its ability to synthesize data that closely resembles the original data source. While Generative Adversarial…
Gigapixel Whole-Slide Images Classification using Locally Supervised Learning
Jingwei Zhang, Xin Zhang, Ke Ma +4
Histopathology whole slide images (WSIs) play a very important role in clinical studies and serve as the gold standard for many cancer diagnoses. However, generating automatic tool…
AI and Pathology: Steering Treatment and Predicting Outcomes
Rajarsi Gupta, Jakub Kaczmarzyk, Soma Kobayashi +2
The combination of data analysis methods, increasing computing capacity, and improved sensors enable quantitative granular, multi-scale, cell-based analyses. We describe the rich s…
Evaluating histopathology transfer learning with ChampKit
Jakub R. Kaczmarzyk, Tahsin M. Kurc, Shahira Abousamra +3
Histopathology remains the gold standard for diagnosis of various cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopat…
Federated Learning for the Classification of Tumor Infiltrating Lymphocytes
Ujjwal Baid, Sarthak Pati, Tahsin M. Kurc +6
We evaluate the performance of federated learning (FL) in developing deep learning models for analysis of digitized tissue sections. A classification application was considered as…
Visual attention analysis of pathologists examining whole slide images of Prostate cancer
Souradeep Chakraborty, Ke Ma, Rajarsi Gupta +4
We study the attention of pathologists as they examine whole-slide images (WSIs) of prostate cancer tissue using a digital microscope. To the best of our knowledge, our study is th…
Multi-Class Cell Detection Using Spatial Context Representation
Shahira Abousamra, David Belinsky, John Van Arnam +7
In digital pathology, both detection and classification of cells are important for automatic diagnostic and prognostic tasks. Classifying cells into subtypes, such as tumor cells,…
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…
Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer
Han Le, Rajarsi Gupta, Le Hou +12
Quantitative assessment of Tumor-TIL spatial relationships is increasingly important in both basic science and clinical aspects of breast cancer research. We have developed and eva…
Label Super Resolution with Inter-Instance Loss
Maozheng Zhao, Le Hou, Han Le +8
For the task of semantic segmentation, high-resolution (pixel-level) ground truth is very expensive to collect, especially for high resolution images such as gigapixel pathology im…