ai4-metadata.yml

metadata_version: 2.0.0
title: Chest x-ray image classifier
summary: Classify chest x-ray images in patological and non patological with this X-ray classifier.
description: |-
  The deep learning revolution has brought significant advances in a number  of fields [1], primarily linked to image and speech recognition. The standardization of image classification tasks like the [ImageNet Large Scale Visual Recognition Challenge](http://www.image-net.org/challenges/LSVRC/) [2] has resulted in a reliable way to compare top performing architectures.

  This Docker container contains the tools to train an image classifier on your personal dataset.

  It is a highly customizable tool  that let's you choose between tens of different [top performing architectures](https://github.com/keras-team/keras-applications) and training  parameters.

  The container also comes with a pretrained general-purpose image classifier trained on ImageNet.

  The PREDICT method expects an RGB image as input (or the url of an RGB image) and will return a JSON with  the top 5 predictions.

  <img class='fit', src='https://raw.githubusercontent.com/deephdc/DEEP-OC-image-classification-tf-dicom/master/images/imagenet.png'/>

  **References**
  
  1. Yann LeCun, Yoshua Bengio, and Geofrey Hinton. [Deep learning](https://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf). Nature, 521(7553):436-444, May 2015.
  2. Olga Russakovsky et al. [ImageNet Large Scale Visual Recognition Challenge](https://arxiv.org/abs/1409.0575). International Journal of Computer Vision (IJCV), 115(3):211-252, 2015.
dates:
  created: '2019-01-01'
  updated: '2024-10-09'
links:
  source_code: https://github.com/deephdc/image-classification-tf-dicom
  docker_image: deephdc/deep-oc-image-classification-tf-dicom
  dataset_url: http://www.image-net.org/challenges/LSVRC/
  training_files_url: https://cephrgw01.ifca.es:8080/swift/v1/imagenet-tf/
  cite_url: http://digital.csic.es/handle/10261/194498
tags:
  - deep learning
tasks:
  - Computer Vision
  - Classification
categories:
  - AI4 trainable
  - AI4 inference
  - AI4 pre trained
libraries:
  - TensorFlow
data-type:
  - Image