Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization

Published July 4, 2024 | Version v1

Journal article Open

Uploading large files of pre trained models through zenodo
Code is availables at:
https://github.com/zaritskylab/DISCOVER

and here: https://zenodo.org/uploads/12657027

Files

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discriminator1D_epoch_1_perc_3.457_CLF_2.411_zcr_0.1276_disent_acc_5.858_disent_loss_0.0_cov_loss_0.99988.keras md5:e824f53a5800a0dbdf1fb9d1beb9f480	260.7 MB	Download
encoder_epoch_1_perc_3.457_CLF_2.411_zcr_0.1276_disent_acc_5.858_disent_loss_0.0_cov_loss_0.99988.keras md5:88e3f7d847a3ae5bc1c758ac885d3f26	50.9 MB	Download
generator_epoch_1_perc_3.457_CLF_2.411_zcr_0.1276_disent_acc_5.858_disent_loss_0.0_cov_loss_0.99988.keras md5:6fddedc5066e6d714be8271bd1fbf138	61.1 MB	Download
recognizer_epoch_1_perc_3.457_CLF_2.411_zcr_0.1276_disent_acc_5.858_disent_loss_0.0_cov_loss_0.99988.keras md5:03e5ffc3fe83e7df31e1579430a35144	13.5 MB	Download
unet_epoch_1_perc_3.457_CLF_2.411_zcr_0.1276_disent_acc_5.858_disent_loss_0.0_cov_loss_0.99988.keras md5:35178a670f0e307605074a74ad59c221	72.4 MB	Download
vgg_seed_3_epoch_64_auc_0.75_gmax_0.73_tpr_0.69_tnr_0.78_acc_0.73_f1_0.72.keras md5:e339f1777c80f864882d8ea06e1f521f	59.1 MB	Download