- Healthcare
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.
- Biomedical 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/
- Up to Speed on Deep Learning in Medical Imaging.
https://medium.com/the-mission/up-to-speed-on-deep-learning-in-medical-imaging-7ff1e91f6d71#.ie2vunuw2 - Radiopedia
- Physionet
- 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/
- DREAM Challenges
- VISCERAL is an abbreviation for Visual Concept Extraction Challenge in Radiology.
- Biomedical Imaging for ISBI Challenges.
- 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