This contains everything which has been listed and works a complete document for things inside. This doesn't work as a map OR indexing file.
- Understanding Semantic Segmentation with UNET. https://towardsdatascience.com/understanding-semantic-segmentation-with-unet-6be4f42d4b47
- Attention Gated Networks (Attention U-net). https://github.com/ozan-oktay/Attention-Gated-Networks
- Yog AI. https://github.com/smellslikeml/YogAI
- U-Net: Convolutional Networks for Biomedical Image Segmentation. https://arxiv.org/pdf/1505.04597.pdf
- Fully Convolutional Networks for Semantic Segmentation. https://arxiv.org/pdf/1411.4038.pdf
- A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation. https://paperswithcode.com/paper/a-novel-focal-tversky-loss-function-with#code
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. Deep Residual Learning for Image Recognition . https://arxiv.org/abs/1512.03385
- The home of the U.S. Government’s open data. https://www.data.gov/
Here you will find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more.
- PubMed
- Lyrn.ai. https://www.lyrn.ai/
- US Government Works. https://www.usa.gov/government-works
Contains info about the Patent Processes and the questions that an individual needs to know while filing for an IPR(Intellectual Property Right).
This field is currently seeing many advancements with the help of AI. There are multiple fields in which researchers are focusing to tackle the problem of automation in the Medical field.
Multiple areas in which researchers are working involve mostly Diagnostics which usually take a lot of time than the actual treatment.
- Biomedcal Segmentation
- Lesion Segmentation
- Brain Tumor Segmentation
- Organ Segmentation
- 3D Medical Imaging Segmentation
- Retina Vessel Segmentation
- Medical Image Classification
- TGS Salt Identification Challenge https://www.kaggle.com/c/tgs-salt-identification-challenge/data
- DRIVE: Digital Retinal Images for Vessel Extraction. http://www.isi.uu.nl/Research/Databases/DRIVE/
- Open Access Biomedical Image Search Engine. https://openi.nlm.nih.gov/
- Attention U-Net: Learning Where to Look for the Pancreas. https://arxiv.org/abs/1804.03999
[1] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional Networks for Biomedical
Image Segmentation. https://arxiv.org/pdf/1505.04597.pdf
[2] Eugenio Culurciello. Neural Network Architectures. https://towardsdatascience.com/neural-network-architectures-156e5bad51ba
[3] Attention U-net [State-of-the-art as of June 2019]
[4] Attention U-Net: Learning Where to Look for the Pancreas https://arxiv.org/pdf/1804.03999.pdf
- ISBI Challenge: Segmentation of neuronal structures in EM stacks. http://brainiac2.mit.edu/isbi_challenge/
- Attention U-net : https://github.com/nabsabraham/focal-tversky-unet
- Retina U-net: https://github.com/orobix/retina-unet
- Pytorch-Unet: https://github.com/milesial/Pytorch-UNet
- Google BERT. https://towardsdatascience.com/bert-explained-state-of-the-art-language-model-for-nlp-f8b21a9b6270
- Word Embeddings and Word2vec. https://towardsdatascience.com/introduction-to-word-embedding-and-word2vec-652d0c2060fa
- Papers with Code. https://paperswithcode.com