Healthcare & Biology Papers

Paper with Code medical section (opens in a new tab): Provides access to research papers along with the corresponding code.

Drug Discovery

• Junction Tree Variational Autoencoder for Molecular Graph Generation (opens in a new tab) (2018)

• Self-Normalizing Neural Networks (opens in a new tab) (2017)

• Neural Message Passing for Quantum Chemistry (opens in a new tab) (2017)

• Gated Graph Sequence Neural Networks (opens in a new tab) (2015)

• Semi-Supervised Classification with Graph Convolutional Networks (opens in a new tab) (2017)

Medical Image Segmentation

• U-Net: Convolutional Networks for Biomedical Image Segmentation (opens in a new tab) (2015)

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (opens in a new tab) (2021)

• SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation (opens in a new tab) (2015)

• Attention U-Net: Learning Where to Look for the Pancreas (opens in a new tab) (2018)

• UNet++: A Nested U-Net Architecture for Medical Image Segmentation (opens in a new tab) (2018)

• V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation (opens in a new tab) (2016)

• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation (opens in a new tab) (2021)

2D Classification

• Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (opens in a new tab) (2017)

• Perceptual Losses for Real-Time Style Transfer and Super-Resolution (opens in a new tab) (2016)

• Image Super-Resolution Using Deep Convolutional Networks (opens in a new tab) (2015)

• SinGAN: Learning a Generative Model from a Single Natural Image (opens in a new tab) (2019)

• Enhanced Deep Residual Networks for Single Image Super-Resolution (opens in a new tab) (2017)

• ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks (opens in a new tab) (2018)

• Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network (opens in a new tab) (2016)

Medical Diagnosis

• BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis (opens in a new tab) (2020)

• Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation (opens in a new tab) (2019)

• DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks (opens in a new tab) (2019)

• Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening (opens in a new tab) (2019)

• The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up (opens in a new tab) (2021)