deep transfer learning model Search Results


deep learning models lvh-net and lvh-net leads i and ii  (Partners HealthCare System Inc)
90
Partners HealthCare System Inc deep learning models lvh-net and lvh-net leads i and ii
Deep Learning Models Lvh Net And Lvh Net Leads I And Ii, supplied by Partners HealthCare System Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep learning models lvh-net and lvh-net leads i and ii - by Bioz Stars, 2026-11
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uncertainty estimation with deep learning for rainfall–runoff modelling  (Gauch GmbH)
90
Gauch GmbH uncertainty estimation with deep learning for rainfall–runoff modelling
Uncertainty Estimation With Deep Learning For Rainfall–Runoff Modelling, supplied by Gauch GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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uncertainty estimation with deep learning for rainfall–runoff modelling - by Bioz Stars, 2026-11
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deep learning model  (Deepmind Technologies Ltd)
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Deepmind Technologies Ltd deep learning model
Deep Learning Model, supplied by Deepmind Technologies Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep learning model - by Bioz Stars, 2026-11
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lung respiratory audio prediction using transfer learning models  (SMAC Corp)
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SMAC Corp lung respiratory audio prediction using transfer learning models
Lung Respiratory Audio Prediction Using Transfer Learning Models, supplied by SMAC Corp, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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lung respiratory audio prediction using transfer learning models - by Bioz Stars, 2026-11
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deep reinforcement learning model  (IEEE Access)
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IEEE Access deep reinforcement learning model
Deep Reinforcement Learning Model, supplied by IEEE Access, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deep+transfer+learning+model/deep+reinforcement+learning+model/10__1007_slash_s10462___024___10706___5-616-4-12
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deep reinforcement learning model - by Bioz Stars, 2026-11
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cgc multitask deep-learning model  (TenCent Inc)
90
TenCent Inc cgc multitask deep-learning model
Cgc Multitask Deep Learning Model, supplied by TenCent Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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cgc multitask deep-learning model - by Bioz Stars, 2026-11
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deep convolutional neural network-based ai platform aiforia create  (Aiforia Technologies)
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Aiforia Technologies deep convolutional neural network-based ai platform aiforia create
Deep Convolutional Neural Network Based Ai Platform Aiforia Create, supplied by Aiforia Technologies, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep convolutional neural network-based ai platform aiforia create - by Bioz Stars, 2026-11
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nvision deep learning animal tracking model  (Inscopix Inc)
90
Inscopix Inc nvision deep learning animal tracking model
Miniscope imaging enables monitoring of vascular dynamic in a freely moving animal model of migraine. (A) A standardized workflow for vascular imaging in a migraine model using miniscopes. Created in BioRender. Ye, T. (2024) BioRender.com/p37×969. (B) A newly developed algorithm enables accurate diameter estimation of a broad range of vessel sizes. First, users manually draw a line that bisects a vessel to be analyzed (left, middle). To estimate the vessel diameter, the algorithm uses the pixel intensity profile along each label, calculating the local minimum on each side of the curve, and comparing to the global maximum. On each side of the curve, the pixel with the value just above the half point between the global maximum and the corresponding minimum is determined to be the edge of the vessel. The distance between the two pixels is calculated as the vessel diameter (right). (C) Comparison of algorithm diameter estimates versus manual estimates of the same vessels. Correlation between the two methods is high (Pearson R = 0.992). (D) Example images before (top left) and 30 min after levcromakalim administration (top right). Orange lines overlaying the images denote the original width of the underlying vessels. Teal lines show the change in vessel width after levcromakalim administration. Traces (below) show the algorithmic diameter estimation of the corresponding vessels in the images above, normalized to the predrug mean. Dashed gray lines indicate the timing of levcromakalim administration. (E) Synchronized behavior videos can be collected alongside miniscope acquisition. The <t>nVision</t> deep learning algorithm can automatically detect a mouse in the camera's field of view (top). Position data can be preserved frame by frame. Mouse position data from the first 5 min of a session is plotted below.
Nvision Deep Learning Animal Tracking Model, supplied by Inscopix Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deep+transfer+learning+model/nvision+deep+learning+animal+tracking+model/pmc11578939-96-11-19
Average 90 stars, based on 1 article reviews
nvision deep learning animal tracking model - by Bioz Stars, 2026-11
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iot and synergic deep learning based tongue color image analysis model  (Tokyo Chemical Industry)
90
Tokyo Chemical Industry iot and synergic deep learning based tongue color image analysis model
Miniscope imaging enables monitoring of vascular dynamic in a freely moving animal model of migraine. (A) A standardized workflow for vascular imaging in a migraine model using miniscopes. Created in BioRender. Ye, T. (2024) BioRender.com/p37×969. (B) A newly developed algorithm enables accurate diameter estimation of a broad range of vessel sizes. First, users manually draw a line that bisects a vessel to be analyzed (left, middle). To estimate the vessel diameter, the algorithm uses the pixel intensity profile along each label, calculating the local minimum on each side of the curve, and comparing to the global maximum. On each side of the curve, the pixel with the value just above the half point between the global maximum and the corresponding minimum is determined to be the edge of the vessel. The distance between the two pixels is calculated as the vessel diameter (right). (C) Comparison of algorithm diameter estimates versus manual estimates of the same vessels. Correlation between the two methods is high (Pearson R = 0.992). (D) Example images before (top left) and 30 min after levcromakalim administration (top right). Orange lines overlaying the images denote the original width of the underlying vessels. Teal lines show the change in vessel width after levcromakalim administration. Traces (below) show the algorithmic diameter estimation of the corresponding vessels in the images above, normalized to the predrug mean. Dashed gray lines indicate the timing of levcromakalim administration. (E) Synchronized behavior videos can be collected alongside miniscope acquisition. The <t>nVision</t> deep learning algorithm can automatically detect a mouse in the camera's field of view (top). Position data can be preserved frame by frame. Mouse position data from the first 5 min of a session is plotted below.
Iot And Synergic Deep Learning Based Tongue Color Image Analysis Model, supplied by Tokyo Chemical Industry, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deep+transfer+learning+model/iot+and+synergic+deep+learning+based+tongue+color+image+analysis+model/10__1109_slash_access__2021__3094226-46-15-14
Average 90 stars, based on 1 article reviews
iot and synergic deep learning based tongue color image analysis model - by Bioz Stars, 2026-11
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u-net deep learning model  (Esri inc)
90
Esri inc u-net deep learning model
Miniscope imaging enables monitoring of vascular dynamic in a freely moving animal model of migraine. (A) A standardized workflow for vascular imaging in a migraine model using miniscopes. Created in BioRender. Ye, T. (2024) BioRender.com/p37×969. (B) A newly developed algorithm enables accurate diameter estimation of a broad range of vessel sizes. First, users manually draw a line that bisects a vessel to be analyzed (left, middle). To estimate the vessel diameter, the algorithm uses the pixel intensity profile along each label, calculating the local minimum on each side of the curve, and comparing to the global maximum. On each side of the curve, the pixel with the value just above the half point between the global maximum and the corresponding minimum is determined to be the edge of the vessel. The distance between the two pixels is calculated as the vessel diameter (right). (C) Comparison of algorithm diameter estimates versus manual estimates of the same vessels. Correlation between the two methods is high (Pearson R = 0.992). (D) Example images before (top left) and 30 min after levcromakalim administration (top right). Orange lines overlaying the images denote the original width of the underlying vessels. Teal lines show the change in vessel width after levcromakalim administration. Traces (below) show the algorithmic diameter estimation of the corresponding vessels in the images above, normalized to the predrug mean. Dashed gray lines indicate the timing of levcromakalim administration. (E) Synchronized behavior videos can be collected alongside miniscope acquisition. The <t>nVision</t> deep learning algorithm can automatically detect a mouse in the camera's field of view (top). Position data can be preserved frame by frame. Mouse position data from the first 5 min of a session is plotted below.
U Net Deep Learning Model, supplied by Esri inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deep+transfer+learning+model/u+net+deep+learning+model/10__1080_slash_01431161__2023__2205983-120-4-16
Average 90 stars, based on 1 article reviews
u-net deep learning model - by Bioz Stars, 2026-11
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convolutional recurrent deep learning model for sentence classification  (IEEE Access)
90
IEEE Access convolutional recurrent deep learning model for sentence classification
Miniscope imaging enables monitoring of vascular dynamic in a freely moving animal model of migraine. (A) A standardized workflow for vascular imaging in a migraine model using miniscopes. Created in BioRender. Ye, T. (2024) BioRender.com/p37×969. (B) A newly developed algorithm enables accurate diameter estimation of a broad range of vessel sizes. First, users manually draw a line that bisects a vessel to be analyzed (left, middle). To estimate the vessel diameter, the algorithm uses the pixel intensity profile along each label, calculating the local minimum on each side of the curve, and comparing to the global maximum. On each side of the curve, the pixel with the value just above the half point between the global maximum and the corresponding minimum is determined to be the edge of the vessel. The distance between the two pixels is calculated as the vessel diameter (right). (C) Comparison of algorithm diameter estimates versus manual estimates of the same vessels. Correlation between the two methods is high (Pearson R = 0.992). (D) Example images before (top left) and 30 min after levcromakalim administration (top right). Orange lines overlaying the images denote the original width of the underlying vessels. Teal lines show the change in vessel width after levcromakalim administration. Traces (below) show the algorithmic diameter estimation of the corresponding vessels in the images above, normalized to the predrug mean. Dashed gray lines indicate the timing of levcromakalim administration. (E) Synchronized behavior videos can be collected alongside miniscope acquisition. The <t>nVision</t> deep learning algorithm can automatically detect a mouse in the camera's field of view (top). Position data can be preserved frame by frame. Mouse position data from the first 5 min of a session is plotted below.
Convolutional Recurrent Deep Learning Model For Sentence Classification, supplied by IEEE Access, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deep+transfer+learning+model/convolutional+recurrent+deep+learning+model+for+sentence+classification/10__1109_slash_access__2021__3084950-343-4-14
Average 90 stars, based on 1 article reviews
convolutional recurrent deep learning model for sentence classification - by Bioz Stars, 2026-11
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a novel twostage deep learning model for efficient network intrusion detection  (IEEE Access)
90
IEEE Access a novel twostage deep learning model for efficient network intrusion detection
Miniscope imaging enables monitoring of vascular dynamic in a freely moving animal model of migraine. (A) A standardized workflow for vascular imaging in a migraine model using miniscopes. Created in BioRender. Ye, T. (2024) BioRender.com/p37×969. (B) A newly developed algorithm enables accurate diameter estimation of a broad range of vessel sizes. First, users manually draw a line that bisects a vessel to be analyzed (left, middle). To estimate the vessel diameter, the algorithm uses the pixel intensity profile along each label, calculating the local minimum on each side of the curve, and comparing to the global maximum. On each side of the curve, the pixel with the value just above the half point between the global maximum and the corresponding minimum is determined to be the edge of the vessel. The distance between the two pixels is calculated as the vessel diameter (right). (C) Comparison of algorithm diameter estimates versus manual estimates of the same vessels. Correlation between the two methods is high (Pearson R = 0.992). (D) Example images before (top left) and 30 min after levcromakalim administration (top right). Orange lines overlaying the images denote the original width of the underlying vessels. Teal lines show the change in vessel width after levcromakalim administration. Traces (below) show the algorithmic diameter estimation of the corresponding vessels in the images above, normalized to the predrug mean. Dashed gray lines indicate the timing of levcromakalim administration. (E) Synchronized behavior videos can be collected alongside miniscope acquisition. The <t>nVision</t> deep learning algorithm can automatically detect a mouse in the camera's field of view (top). Position data can be preserved frame by frame. Mouse position data from the first 5 min of a session is plotted below.
A Novel Twostage Deep Learning Model For Efficient Network Intrusion Detection, supplied by IEEE Access, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/deep+transfer+learning+model/a+novel+twostage+deep+learning+model+for+efficient+network+intrusion+detection/10__1109_slash_access__2021__3121998-492-9-22
Average 90 stars, based on 1 article reviews
a novel twostage deep learning model for efficient network intrusion detection - by Bioz Stars, 2026-11
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Image Search Results


Miniscope imaging enables monitoring of vascular dynamic in a freely moving animal model of migraine. (A) A standardized workflow for vascular imaging in a migraine model using miniscopes. Created in BioRender. Ye, T. (2024) BioRender.com/p37×969. (B) A newly developed algorithm enables accurate diameter estimation of a broad range of vessel sizes. First, users manually draw a line that bisects a vessel to be analyzed (left, middle). To estimate the vessel diameter, the algorithm uses the pixel intensity profile along each label, calculating the local minimum on each side of the curve, and comparing to the global maximum. On each side of the curve, the pixel with the value just above the half point between the global maximum and the corresponding minimum is determined to be the edge of the vessel. The distance between the two pixels is calculated as the vessel diameter (right). (C) Comparison of algorithm diameter estimates versus manual estimates of the same vessels. Correlation between the two methods is high (Pearson R = 0.992). (D) Example images before (top left) and 30 min after levcromakalim administration (top right). Orange lines overlaying the images denote the original width of the underlying vessels. Teal lines show the change in vessel width after levcromakalim administration. Traces (below) show the algorithmic diameter estimation of the corresponding vessels in the images above, normalized to the predrug mean. Dashed gray lines indicate the timing of levcromakalim administration. (E) Synchronized behavior videos can be collected alongside miniscope acquisition. The nVision deep learning algorithm can automatically detect a mouse in the camera's field of view (top). Position data can be preserved frame by frame. Mouse position data from the first 5 min of a session is plotted below.

Journal: Synapse (New York, N.y.)

Article Title: Harnessing Miniscope Imaging in Freely Moving Animals to Unveil Migraine Pathophysiology and Validate Novel Therapeutic Strategies

doi: 10.1002/syn.70001

Figure Lengend Snippet: Miniscope imaging enables monitoring of vascular dynamic in a freely moving animal model of migraine. (A) A standardized workflow for vascular imaging in a migraine model using miniscopes. Created in BioRender. Ye, T. (2024) BioRender.com/p37×969. (B) A newly developed algorithm enables accurate diameter estimation of a broad range of vessel sizes. First, users manually draw a line that bisects a vessel to be analyzed (left, middle). To estimate the vessel diameter, the algorithm uses the pixel intensity profile along each label, calculating the local minimum on each side of the curve, and comparing to the global maximum. On each side of the curve, the pixel with the value just above the half point between the global maximum and the corresponding minimum is determined to be the edge of the vessel. The distance between the two pixels is calculated as the vessel diameter (right). (C) Comparison of algorithm diameter estimates versus manual estimates of the same vessels. Correlation between the two methods is high (Pearson R = 0.992). (D) Example images before (top left) and 30 min after levcromakalim administration (top right). Orange lines overlaying the images denote the original width of the underlying vessels. Teal lines show the change in vessel width after levcromakalim administration. Traces (below) show the algorithmic diameter estimation of the corresponding vessels in the images above, normalized to the predrug mean. Dashed gray lines indicate the timing of levcromakalim administration. (E) Synchronized behavior videos can be collected alongside miniscope acquisition. The nVision deep learning algorithm can automatically detect a mouse in the camera's field of view (top). Position data can be preserved frame by frame. Mouse position data from the first 5 min of a session is plotted below.

Article Snippet: From the behavioral video, animal position was estimated frame‐by‐frame using the nVision deep learning animal tracking model on the Inscopix Data Exploration, Analysis, and Sharing (IDEAS) cloud‐based platform, which was trained on previously collected behavioral data (Figure , bottom).

Techniques: Imaging, Animal Model, Comparison