Search Results for author:
Found 8 papers, 1 papers with code
Symmetric Dense Inception Network for Simultaneous Cell Detection and Classification in Multiplex Immunohistochemistry Images
Deep-learning-based automatic analysis of the multiplex immunohistochemistry (mIHC) enables distinct cell populations to be localized on a large scale, providing insights into disease biology and therapeutic targets.
Automated Quanti cation Of Blood Microvessels In Hematoxylin And Eosin Whole Slide Images
In order to be provided with a supportive micro-environment rich with resources to sustain optimal growth, tumour cells tend to reside in close proximity to a network of blood vessels.
Cell abundance aware deep learning for cell detection on highly imbalanced pathological data
To minimize the effect of cell imbalance on cell detection, we proposed a deep learning pipeline that considers the abundance of cell types during model training.
Glioma Classification Using Multimodal Radiology and Histology Data
The classification algorithm initially carries out tile-level (for histology) and slice-level (for radiology) classification via a deep learning method, then tile/slice-level latent features are combined for a whole-slide and whole-volume sub-type prediction.
ConCORDe-Net: Cell Count Regularized Convolutional Neural Network for Cell Detection in Multiplex Immunohistochemistry Images
Incorporating cell count loss in the objective function regularizes the network to learn weak gradient boundaries and separate weakly stained cells from background artefacts.
Capturing global spatial context for accurate cell classification in skin cancer histology
The SLIC superpixel algorithm was used to segment and classify tumour regions in low resolution H&E-stained histological images of melanoma skin cancer to provide a global context.
DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide images
Subsequently, seeds generated from cell segmentation were propagated to a spatially constrained convolutional neural network for the classification of the cells into stromal, lymphocyte, Ki67-positive cancer cell, and Ki67-negative cancer cell.
Deconvolving convolution neural network for cell detection
Output of the trained CNN is then deconvolved to generate points as cell detection.
