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Machine Learning
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Introduction.

I have decided to list all the most important projects in AI/Machine Learning field that I have published on GitHub.

Image Classification in Healthcare

CXR-Anomaly-Detector

Development of a model based on a Deep Convolutional Network, using a subset of NIH-CXR-14 dataset, to detect if a Chest X-Ray (CXR) contains signs of any diseases.

The model has been trained on TPU, using resources from the Kaggle site.

I have tried to reproduce the results shown in this article from Nature, with very good results.

link to GitHub repository of the project

 

 

CXR-Pneumonia

Development of a model for Pneumonia detection, again based on NIH-CXR-14 dataset.

link to the GitHub repository of the project.

 

Diabetic Retinopathy.

Diabetic Retinopathy is one of the most common and dangerous complications of Diabetes. It is one of the most common causes of blindness in aged people.

Images of the retina can be used to diagnose and monitor the damages made and diagnose this disease. Kaggle in 2015 has launched a competition on this subject.

In this work, I have applied Google EfficientNet in order to see which kind of improvements can be obtained using a state-of-the-art DNN. The results have been really interesting: I could have reached 14th place in the competition.

link to GitHub

see also: https://luigisaetta.it/index.php/deep-learning-ai/43-learning-from-kaggle-competitions

 

Neural Networks for tabular data.

Deployment of a TF 2.3 model using ONNX

In this project I'm using TF 2.3, TF Feature Column API, Keras to develop a Fully Connected Neural Network for binary classification.

Data are coming from Wisconsin Breast Cancer Dataset.

I'm using ONNX as serialization format to explore how easy is to use ONNX for these kinds of models.

see also: https://github.com/luigisaetta/onnxdeployment