AI Medical Compendium Journal:
Applied immunohistochemistry & molecular morphology : AIMM

Showing 1 to 3 of 3 articles

Toward Accurate Deep Learning-Based Prediction of Ki67, ER, PR, and HER2 Status From H&E-Stained Breast Cancer Images.

Applied immunohistochemistry & molecular morphology : AIMM Mar 27, 2025
Despite improvements in machine learning algorithms applied to digital pathology, only moderate accuracy, to predict molecular information from histology alone, has been achieved so far. One of the obstacles is the lack of large data sets to properly...
Humans Female Breast Neoplasms Biomarkers, Tumor Deep Learning Image Processing, Computer-Assisted Staining and Labeling Immunohistochemistry Receptor, ErbB-2 Receptors, Estrogen Receptors, Progesterone Ki-67 Antigen
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Using Deep Learning to Predict Final HER2 Status in Invasive Breast Cancers That are Equivocal (2+) by Immunohistochemistry.

Applied immunohistochemistry & molecular morphology : AIMM Oct 17, 2022
Invasive breast carcinomas are routinely tested for HER2 using immunohistochemistry (IHC), with reflex in situ hybridization (ISH) for those scored as equivocal (2+). ISH testing is expensive, time-consuming, and not universally available. In this st...
Humans Female Breast Neoplasms Biomarkers, Tumor Deep Learning In Situ Hybridization, Fluorescence Immunohistochemistry Receptor, ErbB-2
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Development of a Fully Automated Method to Obtain Reproducible Lymphocyte Counts in Patients With Colorectal Cancer.

Applied immunohistochemistry & molecular morphology : AIMM Jun 15, 2022
Colorectal cancer (CRC) is the third most common cancer worldwide. Although clinical outcome varies among patients diagnosed within the same TNM stage it is the cornerstone in treatment decisions as well as follow-up programmes. Tumor-infiltrating ly...
Humans Artificial Intelligence Colorectal Neoplasms Lymphocytes, Tumor-Infiltrating CD8-Positive T-Lymphocytes Lymphocyte Count
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