209 related articles for article (PubMed ID: 34072131)
1. Investigating the Impact of the Bit Depth of Fluorescence-Stained Images on the Performance of Deep Learning-Based Nuclei Instance Segmentation.
Mahbod A; Schaefer G; Löw C; Dorffner G; Ecker R; Ellinger I
Diagnostics (Basel); 2021 May; 11(6):. PubMed ID: 34072131
[TBL] [Abstract][Full Text] [Related]
2. CryoNuSeg: A dataset for nuclei instance segmentation of cryosectioned H&E-stained histological images.
Mahbod A; Schaefer G; Bancher B; Löw C; Dorffner G; Ecker R; Ellinger I
Comput Biol Med; 2021 May; 132():104349. PubMed ID: 33774269
[TBL] [Abstract][Full Text] [Related]
3. NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images.
Mahbod A; Polak C; Feldmann K; Khan R; Gelles K; Dorffner G; Woitek R; Hatamikia S; Ellinger I
Sci Data; 2024 Mar; 11(1):295. PubMed ID: 38486039
[TBL] [Abstract][Full Text] [Related]
4. CellViT: Vision Transformers for precise cell segmentation and classification.
Hörst F; Rempe M; Heine L; Seibold C; Keyl J; Baldini G; Ugurel S; Siveke J; Grünwald B; Egger J; Kleesiek J
Med Image Anal; 2024 May; 94():103143. PubMed ID: 38507894
[TBL] [Abstract][Full Text] [Related]
5. NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images.
Lal S; Das D; Alabhya K; Kanfade A; Kumar A; Kini J
Comput Biol Med; 2021 Jan; 128():104075. PubMed ID: 33190012
[TBL] [Abstract][Full Text] [Related]
6. MaskMitosis: a deep learning framework for fully supervised, weakly supervised, and unsupervised mitosis detection in histopathology images.
Sebai M; Wang X; Wang T
Med Biol Eng Comput; 2020 Jul; 58(7):1603-1623. PubMed ID: 32445109
[TBL] [Abstract][Full Text] [Related]
7. FEEDNet: a feature enhanced encoder-decoder LSTM network for nuclei instance segmentation for histopathological diagnosis.
Deshmukh G; Susladkar O; Makwana D; Chandra Teja R S; Kumar S N; Mittal S
Phys Med Biol; 2022 Sep; 67(19):. PubMed ID: 35905732
[No Abstract] [Full Text] [Related]
8. Cyclic Learning: Bridging Image-Level Labels and Nuclei Instance Segmentation.
Zhou Y; Wu Y; Wang Z; Wei B; Lai M; Shou J; Fan Y; Xu Y
IEEE Trans Med Imaging; 2023 Oct; 42(10):3104-3116. PubMed ID: 37171933
[TBL] [Abstract][Full Text] [Related]
9. Improving generalization capability of deep learning-based nuclei instance segmentation by non-deterministic train time and deterministic test time stain normalization.
Mahbod A; Dorffner G; Ellinger I; Woitek R; Hatamikia S
Comput Struct Biotechnol J; 2024 Dec; 23():669-678. PubMed ID: 38292472
[TBL] [Abstract][Full Text] [Related]
10. A dual decoder U-Net-based model for nuclei instance segmentation in hematoxylin and eosin-stained histological images.
Mahbod A; Schaefer G; Dorffner G; Hatamikia S; Ecker R; Ellinger I
Front Med (Lausanne); 2022; 9():978146. PubMed ID: 36438040
[TBL] [Abstract][Full Text] [Related]
11. Densely Convolutional Spatial Attention Network for nuclei segmentation of histological images for computational pathology.
Islam Sumon R; Bhattacharjee S; Hwang YB; Rahman H; Kim HC; Ryu WS; Kim DM; Cho NH; Choi HK
Front Oncol; 2023; 13():1009681. PubMed ID: 37305563
[TBL] [Abstract][Full Text] [Related]
12. Weakly Supervised Deep Nuclei Segmentation With Sparsely Annotated Bounding Boxes for DNA Image Cytometry.
Liang Y; Yin Z; Liu H; Zeng H; Wang J; Liu J; Che N
IEEE/ACM Trans Comput Biol Bioinform; 2023; 20(1):785-795. PubMed ID: 34951851
[TBL] [Abstract][Full Text] [Related]
13. Multi-tissue and multi-scale approach for nuclei segmentation in H&E stained images.
Salvi M; Molinari F
Biomed Eng Online; 2018 Jun; 17(1):89. PubMed ID: 29925379
[TBL] [Abstract][Full Text] [Related]
14. Unsupervised domain adaptation for nuclei segmentation: Adapting from hematoxylin & eosin stained slides to immunohistochemistry stained slides using a curriculum approach.
Wang S; Rong R; Gu Z; Fujimoto J; Zhan X; Xie Y; Xiao G
Comput Methods Programs Biomed; 2023 Nov; 241():107768. PubMed ID: 37619429
[TBL] [Abstract][Full Text] [Related]
15. Cx22: A new publicly available dataset for deep learning-based segmentation of cervical cytology images.
Liu G; Ding Q; Luo H; Sha M; Li X; Ju M
Comput Biol Med; 2022 Nov; 150():106194. PubMed ID: 37859287
[TBL] [Abstract][Full Text] [Related]
16. Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images.
Graham S; Vu QD; Raza SEA; Azam A; Tsang YW; Kwak JT; Rajpoot N
Med Image Anal; 2019 Dec; 58():101563. PubMed ID: 31561183
[TBL] [Abstract][Full Text] [Related]
17. Which Pixel to Annotate: A Label-Efficient Nuclei Segmentation Framework.
Lou W; Li H; Li G; Han X; Wan X
IEEE Trans Med Imaging; 2023 Apr; 42(4):947-958. PubMed ID: 36355729
[TBL] [Abstract][Full Text] [Related]
18. Efficient deep learning architecture with dimension-wise pyramid pooling for nuclei segmentation of histopathology images.
Aatresh AA; Yatgiri RP; Chanchal AK; Kumar A; Ravi A; Das D; Bs R; Lal S; Kini J
Comput Med Imaging Graph; 2021 Oct; 93():101975. PubMed ID: 34461375
[TBL] [Abstract][Full Text] [Related]
19. A general deep learning framework for neuron instance segmentation based on Efficient UNet and morphological post-processing.
Wu H; Souedet N; Jan C; Clouchoux C; Delzescaux T
Comput Biol Med; 2022 Nov; 150():106180. PubMed ID: 36244305
[TBL] [Abstract][Full Text] [Related]
20. Robust detection and segmentation of cell nuclei in biomedical images based on a computational topology framework.
Rojas-Moraleda R; Xiong W; Halama N; Breitkopf-Heinlein K; Dooley S; Salinas L; Heermann DW; Valous NA
Med Image Anal; 2017 May; 38():90-103. PubMed ID: 28314191
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
