Faster R-cnn
Faster R-cnn - Related Videos
10:45
A Faster, High Resolution, mtPA-GFP-based Mitochondrial Fusion Assay Acquiring Kinetic Data of Multiple Cells in Parallel Using Confocal Microscopy
0 Views •
Cited by 20 •
2012
Mitochondrial fusion was measured by tracking the equilibration of photoconverted matrix-targeted GFP across the mitochondrial network over time. Thus far, only one cell could be subjected to an hour long kinetic analysis at a time. We present a method that simultaneously measures multiple cells, thereby speeding up the data collection process.
08:19
Subnanometer-Resolution Structural Determination of Hemagglutinin from Cryo-Electron Tomography of Influenza Viruses
0 Views •
Cited by 1 •
2025
This article presents a protocol for data processing of influenza viruses imaged using cryo-electron tomography and subsequent subtomogram averaging of the hemagglutinin glycoprotein. This protocol covers step-by-step data processing, from image preprocessing to final model refinement.
14:03
Using an Automated 3D-tracking System to Record Individual and Shoals of Adult Zebrafish
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Cited by 38 •
2013
The use of a 3D automatic video system that can track individual and groups of zebrafish is described. As application example we explore the effects of the NMDA-receptor antagonist MK-801 on shoals of zebrafish.
07:40
Preparation of Peripheral Blood Mononuclear Cell Pellets and Plasma from a Single Blood Draw at Clinical Trial Sites for Biomarker Analysis
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Cited by 3 •
2021
This protocol details clinically implementable preparation of high quality PBMC and plasma biosamples at the clinical trial site that can be used for translational biomarker analysis.
10:37
Induction and Analysis of Epithelial to Mesenchymal Transition
0 Views •
Cited by 16 •
2013
A straightforward method is described for the induction of epithelial to mesenchymal transition (EMT) in a variety of cell types. Methods for the analysis of cells in EMT states by immunocytochemistry are included.
FAQs
The Region Proposal Network, or RPN, examines features extracted by the convolutional neural network and generates candidate regions that may contain relevant objects. This step narrows later analysis to image areas likely to contain abnormalities. In medical imaging, those proposals can focus subsequent classification and localization on suspected tumors, lesions, fractures, or other findings.
The convolutional neural network first converts the input image into visual features that preserve information useful for recognizing image patterns. RoI pooling then extracts a standardized representation for each proposed region, allowing the detection layers to evaluate regions of different sizes consistently. This connection supports region-level classification and bounding-box refinement within the same analysis pipeline.
Classification determines what a proposed region may represent, while bounding-box refinement improves where that finding is located in the image. Using both outputs provides more than a simple abnormal or normal judgment: it links the predicted category to a specific region. That combined information supports localization and quantitative assessment of findings in medical scans.
Performance depends heavily on the quality of the training data and the accuracy of expert annotation. Annotations provide the reference information needed for the system to learn which regions contain relevant findings and where those regions are located. Consequently, limitations in the data or labeling can affect automated image analysis and the reliability of research results.
A typical workflow begins with supplying a radiographic, histological, or endoscopic image to the convolutional feature extractor. The RPN then proposes candidate regions, after which RoI pooling prepares those regions for the detection layers. Finally, the system classifies each region and refines its bounding box, producing localized predictions for subsequent analysis.
The framework can support detection and localization of tumors, lesions, fractures, and other abnormalities in radiographic, histological, and endoscopic images. Its value lies in identifying where a finding appears as well as assigning it to a predicted category. These outputs can contribute to automated image analysis and quantitative assessment in clinical research.
Faster R-CNN can process medical images systematically and generate localized predictions that researchers use for automated analysis or quantitative assessment. Its usefulness remains tied to the quality of training examples and expert annotations, so the resulting detections should be understood within those data constraints. In this role, it supports clinical research rather than eliminating the need for expert input.