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Found 20 papers, 8 papers with code
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.
Prompt-MIL: Boosting Multi-Instance Learning Schemes via Task-specific Prompt Tuning
Compared to conventional full fine-tuning approaches, we fine-tune less than 1. 3% of the parameters, yet achieve a relative improvement of 1. 29%-13. 61% in accuracy and 3. 22%-27. 18% in AUROC and reduce GPU memory consumption by 38%-45% while training 21%-27% faster.
Precise Location Matching Improves Dense Contrastive Learning in Digital Pathology
Our method outperforms previous dense matching methods by up to 7. 2% in average precision for detection and 5. 6% in average precision for instance segmentation tasks.
Learning Topological Interactions for Multi-Class Medical Image Segmentation
In this paper, we introduce a novel topological interaction module to encode the topological interactions into a deep neural network.
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.
ImageBox3: No-Server Tile Serving to Traverse Whole Slide Images on the Web
Whole slide imaging (WSI) has become the primary modality for digital pathology data.
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.
A Novel Framework for Characterization of Tumor-Immune Spatial Relationships in Tumor Microenvironment
Understanding the impact of tumor biology on the composition of nearby cells often requires characterizing the impact of biologically distinct tumor regions.
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
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.
Identifying Risk of Opioid Use Disorder for Patients Taking Opioid Medications with Deep Learning
Identifying patients at high risk of Opioid Use Disorder (OUD) can help to make early clinical interventions to reduce the risk of OUD.
Representing Whole Slide Cancer Image Features with Hilbert Curves
In this paper we propose using Hilbert Curves simultaneously for spatial indexing and as a polygonal ROI representation.
Exascale Deep Learning to Accelerate Cancer Research
Using MENNDL--an HPC-enabled software stack for neural architecture search--we generate a neural network with comparable accuracy to state-of-the-art networks on a cancer pathology dataset that is also $16\times$ faster at inference.
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.
