​​Understanding Transfer Learning for Medical Imaging

ArXiV: https://arxiv.org/abs/1902.07208

#biolearning #dl #transferlearning

​​Generative Image Translation for Data Augmentation in Colorectal Histopathology Images

#GAN that generates near-real #histology images according to a Turing test with 4 pathologists. The results can be used for training #DL models for detecting rare histological patterns.

ArXiV: https://arxiv.org/abs/1910.05827
Code: https://github.com/BMIRDS/HistoGAN

#CV #healthlearning #biolearning #medical

Using AI to Understand What Causes Diseases

An overview on applying data science in healthcare

Poster: https://info.gnshealthcare.com/hubfs/Publications_2019/ESMO_GI_Final_Poster_Printed_PD_20.pdf
Link: https://hbr.org/2019/11/using-ai-to-understand-what-causes-diseases

#meta #biolearning #dl #medical #healthcare

ProteinNet: a standardized data set for machine learning of protein structure

Link: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2932-0
Github: https://github.com/aqlaboratory/proteinnet

#biolearning #medical #dl

Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach

ArXiV: https://arxiv.org/abs/1912.11027

#Cancer #BreastCancer #DL #CV #biolearning

​​(Re)Discovering Protein Structure and Function Through Language Modeling

Trained solely on unsupervised language modeling, the Transformer's attention mechanism recovers high-level structural (folding) and functional properties of proteins!

Why this is important: traditional protein modelling requires lots of computational power. This might be a key to more efficient structure modelling. Protein structure => function. Function => faster drug research and understanding of diseases mechanisms.

Blog: https://blog.einstein.ai/provis/
Paper: https://arxiv.org/abs/2006.15222
Code: https://github.com/salesforce/provis

#DL #NLU #proteinmodelling #bio #biolearning #insilico

​​Stanford updated tool Stanza with #NER for biomedical and clinical terms

Stanza extended with first domain-specific models for biomedical and clinical medical English. They range from approaching to significantly improving state of the art results on syntactic and NER tasks.

That means that now neural networks are capable of understanding difficult texts with lots of specific terms. That means better search, improved knowledge extraction and approach for performing META analysis, or even research with medical ArXiV publications.

Demo: http://stanza.run/bio
ArXiV: https://arxiv.org/abs/2007.14640

#NLProc #NLU #Stanford #biolearning #medicallearning

​​Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes

#3DRCAN for denoising, super resolution and expansion microscopy.

GitHub: https://github.com/AiviaCommunity/3D-RCAN
ArXiV: https://www.biorxiv.org/content/10.1101/2020.08.27.270439v1

#biolearning #cv #dl

​​DeepMind significally (+100%) improved protein folding modelling

Why is this important: protein folding = protein structure = protein function = how protein works in the living speciment and what it does.
What this means: better vaccines, better meds, more curable diseases and more calamities easen by the medications or better understanding.

Dataset: ~170000 available protein structures from PDB
Hardware: 128 TPUv3 cores (roughly  equivalent to ~100-200 GPUs)

Link: https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology

#DL #NLU #proteinmodelling #bio #biolearning #insilico #deepmind #AlphaFold

🏥Self-supervised Learning for Medical images

Due to standard imaging procedures, medical images (X-ray, CT scans, etc) are usually well aligned.
This paper gives an opportunity to utilize such an alignment to automatically connect similar pairs of images for training.

GitHub: https://github.com/fhaghighi/TransVW
ArXiV: https://arxiv.org/abs/2102.10680

#biolearning #medical #dl #pytorch #keras

Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinity

#Baidu research proposed a structure-aware interactive graph neural network ( #SIGN ) to better learn representations of protein-ligand complexes, since drug discovery relies on the successful prediction of protein-ligand binding affinity.

Link: https://dl.acm.org/doi/10.1145/3447548.3467311

#biolearning #deeplearning

Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study


Some time ago in a different world one of the channel editors shared permmission to use data from sleep & activity tracker Oura Ring to develop an algorithm for COVID-19 prediction.

Results of this study continue to arrive. Today team shared the second manuscript from the first TemPredict Study in Nature Scientific Reports. This manuscript details an algorithm designed to detect COVID-19 using data from the Oura Ring. Alogirthm publication: www.nature.com/articles/s41598-022-07314-0 

The first publication from the first TemPredict Study will continue to be available online for you to access at any time, at this link: https://www.nature.com/articles/s41598-020-78355-6

The first publication from the second TemPredict Study (correlations between data from the Oura Ring and data from a LabCorp antibody blood test) will also continue to be available online for you to access at any time, at this link: https://www.mdpi.com/2076-393X/10/2/264

That's the power of the international collaboration 💪

#oura #covid #biolearning #medical #health