AIMC Topic: Coloring Agents
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Rethinking ImageNet Pre-training for Computational Histopathology.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2022
Transfer learning from ImageNet pretrained weights is widely used when training Deep Learning models on a Histopathology dataset. However, the visual features of the two domains are different. Rather than ImageNet pretrained weights, pre-training on ...
Deep-Learning-Based Evaluation of Dual Stain Cytology for Cervical Cancer Screening: A New Paradigm.
Journal of the National Cancer Institute
Nov 2, 2021
Automated deep learning in ophthalmology: AI that can build AI.
Current opinion in ophthalmology
Sep 1, 2021
PURPOSE OF REVIEW: The purpose of this review is to describe the current status of automated deep learning in healthcare and to explore and detail the development of these models using commercially available platforms. We highlight key studies demons...
Artificial intelligence indocyanine green (ICG) perfusion for colorectal cancer intra-operative tissue classification.
The British journal of surgery
Jan 27, 2021
Multistain segmentation of renal histology: first steps toward artificial intelligence-augmented digital nephropathology.
Kidney international
Jan 1, 2021
Artificial intelligence (AI), and particularly deep learning (DL), are showing great potential in improving pathology diagnostics in many aspects, 1 of which is the segmentation of histology into (diagnostically) relevant compartments. Although most ...
Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study.
The Lancet. Oncology
Jan 1, 2021
BACKGROUND: Detecting microsatellite instability (MSI) in colorectal cancer is crucial for clinical decision making, as it identifies patients with differential treatment response and prognosis. Universal MSI testing is recommended, but many patients...
Phenotype
Reproducibility of Results
Colorectal Neoplasms
Diagnosis, Computer-Assisted
Humans
Predictive Value of Tests
Microsatellite Instability
Coloring Agents
Microscopy
Deep Learning
Genetic Predisposition to Disease
Staining and Labeling
Image Interpretation, Computer-Assisted
Eosine Yellowish-(YS)
Hematoxylin
SWEPT SOURCE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY, FLUORESCEIN ANGIOGRAPHY, AND INDOCYANINE GREEN ANGIOGRAPHY COMPARISONS REVISITED: Using a Novel Deep-Learning-Assisted Approach for Image Registration.
Retina (Philadelphia, Pa.)
Oct 1, 2020
PURPOSE: To compare area measurements between swept source optical coherence tomography angiography (SSOCTA), fluorescein angiography (FA), and indocyanine green angiography (ICGA) after applying a novel deep-learning-assisted algorithm for accurate ...
Objective Assessment of the Utility of Chromoendoscopy with a Support Vector Machine.
Journal of gastrointestinal cancer
Sep 1, 2019
PURPOSE: The utility of chromoendoscopy for early gastric cancer (GC) was determined by machine learning using data of color differences.
Kinetic study of dye removal using TiO supported on polyethylene terephthalate by advanced oxidation processes through neural networks.
Water science and technology : a journal of the International Association on Water Pollution Research
Mar 1, 2019
This work investigated the efficiency of polyethylene terephthalate (PET) as support material for TiO films in the photocatalytic degradation of red Bordeaux and yellow tartrazine dyes. The optimum operating conditions were determined by a factorial ...
Application of the advanced oxidative process on the degradation of the green leaf and purple açaí food dyes with kinetic monitoring and artificial neural network modelling.
Water science and technology : a journal of the International Association on Water Pollution Research
Oct 1, 2018
The study evaluated the advanced oxidative processes concerning the degradation of green leaf and purple açaí dyes, as well as the prediction of data through artificial neural networks (ANNs). It was verified that percentage of degradation on the wav...
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