Search Results for author:
Found 13 papers, 2 papers with code
Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classification
There has been a surge of research in deep learning models for WSI classification with clinical applications such as cancer detection or prediction of molecular mutations from WSIs.
Interpretable histopathology-based prediction of disease relevant features in Inflammatory Bowel Disease biopsies using weakly-supervised deep learning
Finally, we identified several cell level features indicative of disease severity in CD and UC.
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.
Multiple Instance Learning with Auxiliary Task Weighting for Multiple Myeloma Classification
To aid radiological reading, we propose an auxiliary task-based multiple instance learning approach (ATMIL) for MM classification with the ability to localize sites of disease.
Classification of COVID-19 via Homology of CT-SCAN
In this worldwide spread of SARS-CoV-2 (COVID-19) infection, it is of utmost importance to detect the disease at an early stage especially in the hot spots of this epidemic.
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.
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.
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.
Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection
In this paper, we present a semi-supervised mitosis detection method which is designed to leverage a large number of unlabeled breast cancer WSIs.
Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge
The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of $\kappa$ = 0. 567, 95% CI [0. 464, 0. 671] between the predicted scores and the ground truth.
Fast and Accurate Tumor Segmentation of Histology Images using Persistent Homology and Deep Convolutional Features
In this work, we propose a tumor segmentation framework based on the novel concept of persistent homology profiles (PHPs).
Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues
In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring.
