Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection

Related tags

Deep Learningpythondeep-learningkerasmedical-imagingtransfer-learningmultitask-learningdeep-transfer-learning
Overview

Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection

License: GPL v3

Introduction

This repository includes codes and models of "Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection" paper. link: https://doi.org/10.1016/j.compbiomed.2020.104121

Dataset

First you should download the MHSMA dataset using:

git clone https://github.com/soroushj/mhsma-dataset.git

Usage

First of all,the configuration file should be setted.So open dmtl.txt or dtl.txt and set the setting you want.This files contains paramaters of the model that you are going to train.

  • dtl.txt have only one line and contains paramaters to train a DTL model.

  • dmtl.txt contains two lines:paramaters of stage 1 are kept in the first line of the file and paramaters of stage 2 are kept in the second line of the file.
    Some paramaters have an aray of three values that they keep the value of three labels.To set them,consider this sequence:[Acrosome,Vacoule,Head].

  • To train a DTL model,use the following commands and arguments:

python train.py -t dtl [-e epchos] [-label label]  [-model model] [-w file]

Argumetns:

Argument Description
-t type of network(dtl or dmtl)
-e number of epochs
-label label(a,v or h)
-model pre-trained model
-w name of best weihgt file
--phase You can use it to choose stage in DMTL(1 or 2)
--second_model The base model for second stage of DMTL

1.Train

  • To choose a pre-trained model, you can use one of the following models:
model argument Description
vgg_19 VGG 19
vgg_16 VGG 16
resnet_50 Resnet 50
resnet_101 Resnet 101
resnet_502 Resnet 502
  • To train a DMTL model,use the following commands and arguments:
python train.py -t dmtl [--phase phase] [-e epchos] [-label label] [-model model] [-w file]

Also you can use your own pre-trained model by using address of your model instead of the paramaters been told in the table above.

Example:
python train.py -t dmtl --phase 1 -e 100 -label a -model C:\model.h5 -w w.h5

2.K Fold

  • To perform K Fold on a model,use "-k_fold True" argument.
python train.py -k_fold True [-t type] [-e epchos] [-label label] [-model model] [-w file]

3.Threshold Search

  • To find a good threshold for your model,use the following code:
python threshold.py [-t type] [-addr model address] [-l label]

Models

The CNN models that were introduced and evaluated in our research paper can be found in the v1.0 release of this repository.

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Comments
  • a possible typo(bug)

    a possible typo(bug)

    Very interesting idea and complements!

    In LoadData.py, starting from line 150, ` if phase == 'search':

        return {
            "x_train": x_train_128,
            "y_train": y_train,
            "x_train_128": x_train_128,
            'x_val_128': x_valid_128,
            "x_val": x_valid_128,
            "y_val": y_valid,
            "x_test": x_test_128,
            "y_test": y_test
            }`

    here, I think that the first key-value pair should probably be "x_train": x_train instead of "x_train": x_train_128, which causes an error of shape mismatch during fit.

    opened by captainst 0
Releases(v1.0)
Owner
Amir Abbasi
Student at University of Guilan (Computer Engineering), Working on Computer Vision & Reinforcement Learning
Amir Abbasi
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