Mask-RCNN and u-net ensembled for nuclei segmentation

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Tutkijat

Organisaatiot

Kuvaus

Nuclei segmentation is both an important and in some ways ideal task for modern computer vision methods, e.g. convolutional neural networks. While recent developments in theory and open-source software have made these tools easier to implement, expert knowledge is still required to choose the Sight model architecture and training setup. We compare two popular segmentation frameworks, U-Net and Mask-RCNN in the nuclei segmentation task and find that they have different strengths and failures. To get the best of both worlds, we develop an ensemble model to combine their predictions that can outperform both models by a significant margin and should be considered when aiming for best nuclei segmentation performance.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
TilaJulkaistu - 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE International Symposium on Biomedical Imaging - Venice, Italia
Kesto: 8 huhtikuuta 201911 huhtikuuta 2019
Konferenssinumero: 16

Julkaisusarja

NimiIEEE International Symposium on Biomedical Imaging
KustantajaIEEE
ISSN (painettu)1945-7928

Conference

ConferenceIEEE International Symposium on Biomedical Imaging
LyhennettäISBI
MaaItalia
KaupunkiVenice
Ajanjakso08/04/201911/04/2019

ID: 37271211