390 citations · 541 across the 25 of their papers we have counts for
27 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…
Sharing standardized image-derived data in computational pathology using DICOM
Daniela P. Schacherer, Christopher P. Bridge, David Clunie +13
Development and evaluation of computational pathology methods require access to large and diverse datasets. Over the past decade, various initiatives invested significantly into co…
-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…
Learned representation-guided diffusion models for large-image generation
Alexandros Graikos, Srikar Yellapragada, Minh-Quan Le +4
To synthesize high-fidelity samples, diffusion models typically require auxiliary data to guide the generation process. However, it is impractical to procure the painstaking patch-…
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…
PathLDM: Text conditioned Latent Diffusion Model for Histopathology
Srikar Yellapragada, Alexandros Graikos, Prateek Prasanna +3
To achieve high-quality results, diffusion models must be trained on large datasets. This can be notably prohibitive for models in specialized domains, such as computational pathol…
SAM-Path: A Segment Anything Model for Semantic Segmentation in Digital Pathology
Jingwei Zhang, Ke Ma, Saarthak Kapse +4
Semantic segmentations of pathological entities have crucial clinical value in computational pathology workflows. Foundation models, such as the Segment Anything Model (SAM), have…
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…
Prompt-MIL: Boosting Multi-Instance Learning Schemes via Task-specific Prompt Tuning
Jingwei Zhang, Saarthak Kapse, Ke Ma +4
Whole slide image (WSI) classification is a critical task in computational pathology, requiring the processing of gigapixel-sized images, which is challenging for current deep-lear…
Precise Location Matching Improves Dense Contrastive Learning in Digital Pathology
Jingwei Zhang, Saarthak Kapse, Ke Ma +4
Dense prediction tasks such as segmentation and detection of pathological entities hold crucial clinical value in computational pathology workflows. However, obtaining dense annota…
Learning Topological Interactions for Multi-Class Medical Image Segmentation
Saumya Gupta, Xiaoling Hu, James Kaan +10
Deep learning methods have achieved impressive performance for multi-class medical image segmentation. However, they are limited in their ability to encode topological interactions…
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…
A Novel Framework for Characterization of Tumor-Immune Spatial Relationships in Tumor Microenvironment
Mahmudul Hasan, Jakub R. Kaczmarzyk, David Paredes +9
Understanding the impact of tumor biology on the composition of nearby cells often requires characterizing the impact of biologically distinct tumor regions. Biomarkers have been d…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
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…
An Open Natural Language Processing Development Framework for EHR-based Clinical Research: A case demonstration using the National COVID Cohort Collaborative (N3C)
Sijia Liu, Andrew Wen, Liwei Wang +28
While we pay attention to the latest advances in clinical natural language processing (NLP), we can notice some resistance in the clinical and translational research community to a…
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…