TensorFlow-MIL
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TF Implementation of Multiple Instance Learning FCN.
Deep Multiple Instance Learning
This repository contains a TensorFlow implementation of the CNN-MIL combination described in Classifying and Segmenting Microscopy Images with Deep Multiple Instance Learning from Brendan Frey`s lab.
Getting Started
- Install requirements (will install CPU version of TensorFlow):
pip install requirements.txt
. - Follow instructions on (tf_cnnvis)[https://github.com/InFoCusp/tf_cnnvis] README.
tf_cnnvis/
should be at the root after installation.
Options
The following options are available for running the model:
-
-e
, Number of epochs for which to train the model -
-r
, Specify the seed -
-b
, Batch size for training -
-s
, Where to save model -
-m
, Name of model -
-t
, Whether to train (1) or load model (0)
Datasets
The datasets.py
file contains a small cluttered MNIST dataset. Each
image is 72 x 72 pixels and contains four numbers: three are 0's, the other one is a number
1 - 9 excluding 0. The locations of these numbers in the image are semi-random.
This dataset is a much smaller version of what the authors use in the paper.