NETVATION DL MEDICINE
NetVation DL Medicine is a Chengdu, China based biopharmaceutical company dedicated to advancing novel therapeutic entities into the clinic for Metabolic, Inflammatory and Cancer related diseases. NetVation DL Medicine (DL Medicine) has a strategic partnership with HitGen Ltd around accessing and integrating HitGen's DNA-encoded library (DEL) technology platform of over 150 billion compounds for use against DL Medicine targets. To best utilize HitGen's world leading DEL technology, DL Medicine i... s developing a proprietary drug discovery platform to identify novel, potent chemical lead matter with excellent drug-like properties as initial starting points for lead development. This platform will be used in screening against both standard druggable targets as well as unconventional (i.e. classical undruggable) targets. DL Medicine will also utilize the DEL platform to aid in the validation of biological pathways through specific target intervention methodologies. By accessing the emerging R&D landscape in China, which consists of strong government support and a rapidly developing market and economy, including a highly professional CRO and CMO industry, DL Medicine is positioned as a resource-efficient discovery operation utilizing the enormous capabilities of the Asia-Pacific biotechnology region.
NETVATION DL MEDICINE
Industry:
Biotechnology Chemical Health Care Pharmaceutical
Address:
Chengdu, Sichuan, China
Country:
China
Website Url:
http://www.nvdlmed.com
Status:
Active
Contact:
+86-15982870349
Email Addresses:
[email protected]
Technology used in webpage:
Nginx Baidu Analytics Energy Group Hosting
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http://www.nvdlmed.com
- Host name: 172.67.202.181
- IP address: 172.67.202.181
- Location: United States
- Latitude: 37.751
- Longitude: -97.822
- Timezone: America/Chicago
More informations about "NetVation DL Medicine"
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NetVation DL Medicine is a Chengdu, China based biopharmaceutical company dedicated to advancing novel therapeutic entities into the clinic for Metabolic, Inflammatory and Cancer related diseases. NetVation DL Medicine (DL …See details»
The Medical Segmentation Decathlon | Nature Communications
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[2106.05735] The Medical Segmentation Decathlon - ar5iv
The key idea of NVDLMED’s method was to use a fully-supervised uncertainty-aware multi-view co-training strategy [26]. They achieved robustness and generalization by initializing the model …See details»
GitHub - IAmSuyogJadhav/3d-mri-brain-tumor-segmentation …
The author (team name: NVDLMED) ranked #1 on the BraTS 2018 leaderboard using the model described in the paper. This repository contains the model complete with the loss function, all …See details»
(PDF) The Medical Segmentation Decathlon - ResearchGate
Jul 15, 2022 · Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent …See details»
Uncertainty-aware multi-view co-training for semi-supervised …
Oct 1, 2020 · Under fully supervised training, our team NVDLMED was ranked the 3rd place in the first phase and the 2nd place in the final validation phase of Medical Segmentation Decathlon …See details»
Multi-Task Learning for Small Brain Tumor Segmentation from …
Note that the No New-Net ranked second in the BraTS2018 challenge and its performance on the whole tumor is better than others. However, it is worth knowing that they trained their models …See details»
Robustness of brain tumor segmentation - PMC - National Center …
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3D MRI Brain Tumor Segmentation Using Autoencoder …
Multimodal Brain Tumor Segmentation Challenge (BraTS) aims to evaluate state-of-the-art methods for the segmentation of brain tumors by providing a 3D MRI dataset with ground truth …See details»
(PDF) A Deep Learning Framework Integrating MRI Image
Oct 31, 2022 · Here, we employed a deep learning pipeline with three essential steps: (1) MRI images were segmented using preprocessing approaches and UNet architecture, (2) brain …See details»
The Medical Segmentation Decathlon - PMC - National Center for ...
Jul 15, 2022 · The key idea of NVDLMED’s method was to use a fully-supervised uncertainty-aware multi-view co-training strategy 14. They achieved robustness and generalization by …See details»
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Aug 1, 2021 · Compared to the state-of-the-art model NVDLMED [17], the Dice similarity coefficients of our proposed MVHS-Net are only 1.51%, 1.13%, and 0.78% on ET, WT, and …See details»
3D Dilated Multi-fiber Network for Real-Time Brain Tumor
Oct 10, 2019 · Our proposed DMFNet achieves scores of 80.12%, 90.62% and 84.54% for ET, WT and TC, respectively. Compared to the best scores achieved by NVDLMED (single …See details»
DGRUnit: Dual graph reasoning unit for brain tumor segmentation
Oct 1, 2022 · Our method outperforms its backbone Nested U-net on all three regions, with improvements of 7.4% on ET Dice, 1.3% on WT Dice and 4.6% on TC Dice. When compared …See details»
Convolutional Neural Networks for Multimodal Brain MRI Images ...
Jun 4, 2020 · This algorithm is the most ranked one for BRATS 18 challenge named NVDLMED method introduced by ANDRIY MYRONENKO. BRATS 2018 training dataset included 285 …See details»
easthorse/brain-tumor-segmentation-based-on-group-convolution
Overview. Brain tumor is a serious threat to human health. The invasive growth of brain tumor, when it occupies a certain space in the skull, will lead to increased intracranial pressure and …See details»
A Deep‐Learning Model with Learnable Group Convolution and …
Feb 10, 2021 · Myronenko proposed an encoder-decoder architecture network, NVDLMED, added another decoder pathway to recover input images, and imposed additional constraints. …See details»
A Deep‐Learning Model with Learnable Group Convolution and …
R s than No t d NVDLMED, e g s in e S 2018 challenge. 1e segmentation n of e proposed method with regard to e , g , d r s s , , d . Furthermore, e proposed method uses r parameters d a less …See details»
CASPIANET++: A multidimensional Channel-Spatial Asymmetric …
Sep 1, 2021 · NVDLMED, with its main and auxiliary branch, utilizes the most FLOP and parameters compared to other schemes. In contrast, CASPIANET++ uses 98% less FLOPs …See details»
3D Dilated Multi-Fiber Network for Real-time Brain Tumor ... - ar5iv
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