AIMC Topic: Immunohistochemistry
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Discriminant analysis and machine learning approach for evaluating and improving the performance of immunohistochemical algorithms for COO classification of DLBCL.
Journal of translational medicine
Jun 11, 2019
BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is classified into germinal center-like (GCB) and non-germinal center-like (non-GCB) cell-of-origin groups, entities driven by different oncogenic pathways with different clinical outcomes. DLBCL clas...
Breast cancer outcome prediction with tumour tissue images and machine learning.
Breast cancer research and treatment
May 22, 2019
PURPOSE: Recent advances in machine learning have enabled better understanding of large and complex visual data. Here, we aim to investigate patient outcome prediction with a machine learning method using only an image of tumour sample as an input.
Female
Aged
Aged, 80 and over
Adult
Humans
Machine Learning
Prognosis
Workflow
Neoplasm Metastasis
Microscopy
Breast Neoplasms
Image Processing, Computer-Assisted
Immunohistochemistry
Survival Analysis
Biomarkers, Tumor
Follow-Up Studies
Middle Aged
Tumor Burden
Kaplan-Meier Estimate
Neoplasm Staging
Neoplasm Grading
A Fully Automated System Using A Convolutional Neural Network to Predict Renal Allograft Rejection: Extra-validation with Giga-pixel Immunostained Slides.
Scientific reports
Mar 26, 2019
Pathologic diagnoses mainly depend on visual scoring by pathologists, a process that can be time-consuming, laborious, and susceptible to inter- and/or intra-observer variations. This study proposes a novel method to enhance pathologic scoring of ren...
Learning Where to See: A Novel Attention Model for Automated Immunohistochemical Scoring.
IEEE transactions on medical imaging
Mar 22, 2019
Estimating over-amplification of human epidermal growth factor receptor 2 (HER2) on invasive breast cancer is regarded as a significant predictive and prognostic marker. We propose a novel deep reinforcement learning (DRL)-based model that treats imm...
Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis.
Trends in cancer
Feb 28, 2019
Deep learning refers to a set of computer models that have recently been used to make unprecedented progress in the way computers extract information from images. These algorithms have been applied to tasks in numerous medical specialties, most exten...
Pixel-to-Pixel Learning With Weak Supervision for Single-Stage Nucleus Recognition in Ki67 Images.
IEEE transactions on bio-medical engineering
Feb 22, 2019
OBJECTIVE: Nucleus recognition is a critical yet challenging step in histopathology image analysis, for example, in Ki67 immunohistochemistry stained images. Although many automated methods have been proposed, most use a multi-stage processing pipeli...
High-Quality Immunohistochemical Stains Through Computational Assay Parameter Optimization.
IEEE transactions on bio-medical engineering
Feb 13, 2019
Accurate profiling of tumors using immunohistochemistry (IHC) is essential in cancer diagnosis. The inferences drawn from IHC-stained images depend to a great extent on the quality of immunostaining, which is in turn affected strongly by assay parame...
Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images.
Scientific reports
Feb 7, 2019
Multiparametric magnetic resonance imaging (mpMRI) has become increasingly important for the clinical assessment of prostate cancer (PCa), but its interpretation is generally variable due to its relatively subjective nature. Radiomics and classificat...
Cellular cartography of the organ of Corti based on optical tissue clearing and machine learning.
eLife
Jan 18, 2019
The highly organized spatial arrangement of sensory hair cells in the organ of Corti is essential for inner ear function. Here, we report a new analytical pipeline, based on optical clearing of tissue, for the construction of a single-cell resolution...
Deep Semi Supervised Generative Learning for Automated Tumor Proportion Scoring on NSCLC Tissue Needle Biopsies.
Scientific reports
Nov 26, 2018
The level of PD-L1 expression in immunohistochemistry (IHC) assays is a key biomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) patients that may respond to anti PD-1/PD-L1 treatments. The quantification of PD-L1 expression current...
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