Resnet-50 Backbone
Resnet-50 Backbone - Related Videos
08:26
Measuring the 50% Haemolytic Complement (CH50) Activity of Serum
0 Views •
Cited by 91 •
2010
The classical pathway is activated by antibody and culminates in target cell lysis. The CH50 assay provides a measure of the complement activity of a serum sample. This video demonstrates the steps involved in determining the CH50 of a serum sample, the calculations and interpreting of results.
08:48
Development of a Backbone Cyclic Peptide Library as Potential Antiparasitic Therapeutics Using Microwave Irradiation
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Cited by 10 •
2016
A simple and general method for the synthesis of cyclic peptides using microwave irradiation is outlined. This procedure enables the synthesis of backbone cyclic peptides with a collection of different conformations while retaining the side chains and the pharmacophoric moieties., and therefore, allows to screen for the bioactive conformation.
10:31
Residue-Specific Exchange of Proline by Proline Analogs in Fluorescent Proteins: How "Molecular Surgery" of the Backbone Affects Folding and Stability
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Cited by 6 •
2022
To overcome the limitations of classical site-directed mutagenesis, proline analogs with specific modifications were incorporated into several fluorescent proteins. We show how the replacement of hydrogen by fluorine or of the single by double bonds in proline residues ("molecular surgery") affects fundamental protein properties, including their folding and interaction with light.
11:13
Creating Sub-50 Nm Nanofluidic Junctions in PDMS Microfluidic Chip via Self-Assembly Process of Colloidal Particles
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Cited by 2 •
2016
We propose a simple self-assembly technique of silica colloidal nanoparticles to create a nanofluidic junction between two microchannels in polydimethylsiloxane (PDMS). Using this technique, a nanoporous bead membrane with a pore size down to ~45 nm was built inside a microchannel and applied to electrokinetic preconcentration of DNA samples.
07:22
Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes
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2025
Here, we present a protocol to assess glycemic control using capillary blood glucose (CBG) and glycated hemoglobin A1C (HbA1C) levels. This study investigates the impact of hyperglycemia on knee osteoarthritis (KOA) symptoms, physical performance, physical activity level, radiographic severity, and inflammation in older adults with diabetes.
FAQs
Shortcut connections help deep ResNet-50 networks preserve usable training signals by adding a block’s input directly to its output. This direct path supports gradient propagation through the network, reducing the difficulty of optimizing successive transformations as depth increases. The block can therefore concentrate on learning refinements to an existing representation, supporting reliable feature extraction in engineering vision systems.
Convolutional stages progressively transform an image into hierarchical feature maps, allowing the network to represent visual information at successive levels. Downsampling changes the representation as processing proceeds, while later features can support broader interpretation of image content. This structured progression gives downstream systems a feature basis for tasks such as classification, detection, segmentation, and visual inspection.
Normalization and nonlinear activation support the transformation performed within each convolutional stage. Normalization helps organize intermediate representations, while nonlinear activation enables the network to learn more expressive patterns than a sequence of linear operations could provide. Combined with shortcut connections, these components let each block modify existing features while retaining a path for the original information.
A typical workflow passes an image through the architecture’s convolution, normalization, activation, and downsampling stages to produce hierarchical feature maps. A task-specific system then uses those extracted features for classification, object detection, image segmentation, or visual inspection. The backbone therefore separates general visual representation from the downstream engineering task that interprets it.
Transfer learning provides a practical way to adapt ResNet-50 to specialized datasets. Its established visual representations can serve as a starting point, after which the model is adjusted for the target engineering application. This approach uses the architecture’s modular design to connect general feature extraction with task-specific data, supporting applications where the available dataset differs from broad visual datasets.
The extracted feature maps can support several engineering computer-vision functions, including image classification, object detection, image segmentation, and visual inspection. These tasks require the system to identify, locate, separate, or assess visual content. Using one modular backbone across these functions can provide a consistent representation while allowing the downstream system to match the intended inspection or analysis outcome.