PMID- 37079410
OWN - NLM
STAT- MEDLINE
DCOM- 20231003
LR  - 20231003
IS  - 1558-254X (Electronic)
IS  - 0278-0062 (Linking)
VI  - 42
IP  - 10
DP  - 2023 Oct
TI  - KaryoNet: Chromosome Recognition With End-to-End Combinatorial Optimization 
      Network.
PG  - 2899-2911
LID - 10.1109/TMI.2023.3268889 [doi]
AB  - Chromosome recognition is a critical way to diagnose various hematological 
      malignancies and genetic diseases, which is however a repetitive and 
      time-consuming process in karyotyping. To explore the relative relation between 
      chromosomes, in this work, we start from a global perspective and learn the 
      contextual interactions and class distribution features between chromosomes 
      within a karyotype. We propose an end-to-end differentiable combinatorial 
      optimization method, KaryoNet, which captures long-range interactions between 
      chromosomes with the proposed Masked Feature Interaction Module (MFIM) and 
      conducts label assignment in a flexible and differentiable way with Deep 
      Assignment Module (DAM). Specially, a Feature Matching Sub-Network is built to 
      predict the mask array for attention computation in MFIM. Lastly, Type and 
      Polarity Prediction Head can predict chromosome type and polarity simultaneously. 
      Extensive experiments on R-band and G-band two clinical datasets demonstrate the 
      merits of the proposed method. For normal karyotypes, the proposed KaryoNet 
      achieves the accuracy of 98.41% on R-band chromosome and 99.58% on G-band 
      chromosome. Owing to the extracted internal relation and class distribution 
      features, KaryoNet can also achieve state-of-the-art performances on karyotypes 
      of patients with different types of numerical abnormalities. The proposed method 
      has been applied to assist clinical karyotype diagnosis. Our code is available 
      at: https://github.com/xiabc612/KaryoNet.
FAU - Xia, Chao
AU  - Xia C
FAU - Wang, Jiyue
AU  - Wang J
FAU - Qin, Yulei
AU  - Qin Y
FAU - Wen, Juan
AU  - Wen J
FAU - Liu, Zhaojiang
AU  - Liu Z
FAU - Song, Ning
AU  - Song N
FAU - Wu, Lingqian
AU  - Wu L
FAU - Chen, Bing
AU  - Chen B
FAU - Gu, Yun
AU  - Gu Y
FAU - Yang, Jie
AU  - Yang J
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20231002
PL  - United States
TA  - IEEE Trans Med Imaging
JT  - IEEE transactions on medical imaging
JID - 8310780
SB  - IM
MH  - Humans
MH  - *Chromosomes/genetics
MH  - Karyotyping
EDAT- 2023/04/20 13:41
MHDA- 2023/10/03 06:47
CRDT- 2023/04/20 12:23
PHST- 2023/10/03 06:47 [medline]
PHST- 2023/04/20 13:41 [pubmed]
PHST- 2023/04/20 12:23 [entrez]
AID - 10.1109/TMI.2023.3268889 [doi]
PST - ppublish
SO  - IEEE Trans Med Imaging. 2023 Oct;42(10):2899-2911. doi: 10.1109/TMI.2023.3268889. 
      Epub 2023 Oct 2.