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Found 15 papers, 2 papers with code
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
Mimicking a Pathologist: Dual Attention Model for Scoring of Gigapixel Histology Images
The second component is a hard attention classification model, which further extracts a sequence of multi-resolution glimpses from each tile for classification.
Consistency Regularisation in Varying Contexts and Feature Perturbations for Semi-Supervised Semantic Segmentation of Histology Images
The proposed method incorporates context-aware consistency by contrasting pairs of overlapping images in a pixel-wise manner from changing contexts resulting in robust and context invariant features.
FedDropoutAvg: Generalizable federated learning for histopathology image classification
In this study, we propose FedDropoutAvg, a new federated learning approach for training a generalizable model.
Simultaneous Nuclear Instance and Layer Segmentation in Oral Epithelial Dysplasia
Oral epithelial dysplasia (OED) is a pre-malignant histopathological diagnosis given to lesions of the oral cavity.
All You Need is Color: Image based Spatial Gene Expression Prediction using Neural Stain Learning
"Is it possible to predict expression levels of different genes at a given spatial location in the routine histology image of a tumor section by modeling its stain absorption characteristics?"
L1-regularized neural ranking for risk stratification and its application to prediction of time to distant metastasis in luminal node negative chemotherapy naïve breast cancer patients
Can we predict if an early stage cancer patient is at high risk of developing distant metastasis and what clinicopathological factors are associated with such a risk?
ALBRT: Cellular Composition Prediction in Routine Histology Images
Cellular composition prediction, i. e., predicting the presence and counts of different types of cells in the tumor microenvironment from a digitized image of a Hematoxylin and Eosin (H&E) stained tissue section can be used for various tasks in computational pathology such as the analysis of cellular topology and interactions, subtype prediction, survival analysis, etc.
HydraMix-Net: A Deep Multi-task Semi-supervised Learning Approach for Cell Detection and Classification
The model is trained in multi-task learning manner with noise tolerant joint loss for classification localization and achieves better performance when given limited data in contrast to a simple deep model.
Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images
Digital histology images are amenable to the application of convolutional neural network (CNN) for analysis due to the sheer size of pixel data present in them.
Learning Where to See: A Novel Attention Model for Automated Immunohistochemical Scoring
Estimating over-amplification of human epidermal growth factor receptor 2 (HER2) on invasive breast cancer (BC) is regarded as a significant predictive and prognostic marker.
Micro-Net: A unified model for segmentation of various objects in microscopy images
Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images.
Ranked #13 on
Multi-tissue Nucleus Segmentation
on Kumar
Representation-Aggregation Networks for Segmentation of Multi-Gigapixel Histology Images
Convolutional Neural Network (CNN) models have become the state-of-the-art for most computer vision tasks with natural images.
Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest
Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer.
Assessment of algorithms for mitosis detection in breast cancer histopathology images
The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers.
