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Mehul Sampat's Projects

mask_rcnn icon mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

milk icon milk

MILK: Machine Learning Toolkit

milksets icon milksets

Machine Learning Toolkit Datasets: A collection of UCI datasets with a Python interface

mle-flashcards icon mle-flashcards

200+ detailed flashcards useful for reviewing topics in machine learning, computer vision, and computer science.

mlrf icon mlrf

Machine Learning Research Flashcards (for Anki)

monai-deploy-app-sdk icon monai-deploy-app-sdk

MONAI Deploy App SDK offers a framework and associated tools to design, develop and verify AI-driven applications in the healthcare imaging domain.

nucleidetectron icon nucleidetectron

7th place solution for the 2018 Data Science Bowl with a score of 0.591

paper_reading_list icon paper_reading_list

Recommended Papers. Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Learning (cs.LG)

pixel_rnn icon pixel_rnn

Working Theano implementation of Pixel RNN on MNIST.

pneumonia-diagnosis-using-xrays-96-percent-recall icon pneumonia-diagnosis-using-xrays-96-percent-recall

BEST SCORE ON KAGGLE SO FAR , EVEN BETTER THAN THE KAGGLE TEAM MEMBER WHO DID BEST SO FAR. The project is about diagnosing pneumonia from XRay images of lungs of a person using self laid convolutional neural network and tranfer learning via inceptionV3. The images were of size greater than 1000 pixels per dimension and the total dataset was tagged large and had a space of 1GB+ . My work includes self laid neural network which was repeatedly tuned for one of the best hyperparameters and used variety of utility function of keras like callbacks for learning rate and checkpointing. Could have augmented the image data for even better modelling but was short of RAM on kaggle kernel. Other metrics like precision , recall and f1 score using confusion matrix were taken off special care. The other part included a brief introduction of transfer learning via InceptionV3 and was tuned entirely rather than partially after loading the inceptionv3 weights for the maximum achieved accuracy on kaggle till date. This achieved even a higher precision than before.

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