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Found 18 papers, 6 papers with code
Decoding the visual attention of pathologists to reveal their level of expertise
We present a method for classifying the expertise of a pathologist based on how they allocated their attention during a cancer reading.
Open and reusable deep learning for pathology with WSInfer and QuPath
The field of digital pathology has seen a proliferation of deep learning models in recent years.
Halcyon -- A Pathology Imaging and Feature analysis and Management System
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 production of features from deep-learning feature pipelines.
Topology-Guided Multi-Class Cell Context Generation for Digital Pathology
In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis.
ViT-DAE: Transformer-driven Diffusion Autoencoder for Histopathology Image Analysis
This marks the first time that ViT has been introduced to diffusion autoencoders in computational pathology, allowing the model to better capture the complex and intricate details of histopathology images.
Gigapixel Whole-Slide Images Classification using Locally Supervised Learning
Histopathology whole slide images (WSIs) play a very important role in clinical studies and serve as the gold standard for many cancer diagnoses.
AI and Pathology: Steering Treatment and Predicting Outcomes
The combination of data analysis methods, increasing computing capacity, and improved sensors enable quantitative granular, multi-scale, cell-based analyses.
Evaluating histopathology transfer learning with ChampKit
Histopathology remains the gold standard for diagnosis of various cancers.
Federated Learning for the Classification of Tumor Infiltrating Lymphocytes
We evaluate the performance of federated learning (FL) in developing deep learning models for analysis of digitized tissue sections.
Visual attention analysis of pathologists examining whole slide images of Prostate cancer
To quantify the relationship between a pathologist's attention and evidence for cancer in the WSI, we obtained tumor annotations from a genitourinary specialist.
Multi-Class Cell Detection Using Spatial Context Representation
In digital pathology, both detection and classification of cells are important for automatic diagnostic and prognostic tasks.
A Joint Spatial and Magnification Based Attention Framework for Large Scale Histopathology Classification
To tackle these problems, we propose a novel spatial and magnification based attention sampling strategy.
Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types
To address this, we developed an analysis pipeline that segments nuclei in whole slide tissue images from multiple cancer types with a quality control process.
Learning from Thresholds: Fully Automated Classification of Tumor Infiltrating Lymphocytes for Multiple Cancer Types
This allows for a much larger training set, that reflects visual variability across multiple cancer types and thus training of a single network which can be automatically applied to each cancer type without human adjustment.
Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer
Quantitative assessment of Tumor-TIL spatial relationships is increasingly important in both basic science and clinical aspects of breast cancer research.
Label Super Resolution with Inter-Instance Loss
On the other hand, collecting low resolution labels (labels for a block of pixels) for these high resolution images is much more cost efficient.
Disease phenotyping using deep learning: A diabetes case study
Characterization of a patient clinical phenotype is central to biomedical informatics.
Methods for Segmentation and Classification of Digital Microscopy Tissue Images
Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis.
