nyukat/3D_GMIC

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Created about 2 years ago
Updated about 2 years ago

nyukat/3D_GMIC

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3D-GMIC: an efficient deep neural network to find small objects in large 3D images

breast_cancer_classifier

Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening

Jupyter Notebook

breast_density_classifier

Breast density classification with deep convolutional neural networks

GMIC

An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization

Jupyter Notebook

BIRADS_classifier

High-resolution breast cancer screening with multi-view deep convolutional neural networks

mammography_metarepository

Meta-repository of screening mammography classifiers

GLAM

Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis

Jupyter Notebook

COVID-19_prognosis

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

Jupyter Notebook

perception_comparison

Differences between human and machine perception in medical diagnosis

Jupyter Notebook

greedy_multimodal_learning

Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks

pathology_extraction

Improving Information Extraction from Pathology Reports using Named Entity Recognition

MRI_AI

This repository contains code that was used to train and evaluate deep learning models, as described in the article "Improving breast cancer diagnostics with artificial intelligence for MRI" by Jan Witowski et al.

Jupyter Notebook

generative-multitask-learning

This repository contains the code necessary to reproduce our paper "Generative multitask learning mitigates target-causing confounding" https://arxiv.org/abs/2202.04136.

incidental-correlation