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

Last update: Sep 05, 2022

Overview

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

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)

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

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

Related tags

Overview

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

Introduction

Dataset

Usage

1.Train

2.K Fold

3.Threshold Search

Models

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Releases(v1.0)

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