160 related articles for article (PubMed ID: 38292472)
1. 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]
2. Nuclei instance segmentation from histopathology images using Bayesian dropout based deep learning.
Gudhe NR; Kosma VM; Behravan H; Mannermaa A
BMC Med Imaging; 2023 Oct; 23(1):162. PubMed ID: 37858043
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. 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]
6. 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]
7. 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]
8. 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]
9. The role of unpaired image-to-image translation for stain color normalization in colorectal cancer histology classification.
Altini N; Marvulli TM; Zito FA; Caputo M; Tommasi S; Azzariti A; Brunetti A; Prencipe B; Mattioli E; De Summa S; Bevilacqua V
Comput Methods Programs Biomed; 2023 Jun; 234():107511. PubMed ID: 37011426
[TBL] [Abstract][Full Text] [Related]
10. Recursive Training Strategy for a Deep Learning Network for Segmentation of Pathology Nuclei With Incomplete Annotation.
Zhou C; Chan HP; Hadjiiski LM; Chughtai A
IEEE Access; 2022; 10():49337-49346. PubMed ID: 35665366
[TBL] [Abstract][Full Text] [Related]
11. Learning Domain-Invariant Representations of Histological Images.
Lafarge MW; Pluim JPW; Eppenhof KAJ; Veta M
Front Med (Lausanne); 2019; 6():162. PubMed ID: 31380377
[TBL] [Abstract][Full Text] [Related]
12. Trophectoderm segmentation in human embryo images via inceptioned U-Net.
Rad RM; Saeedi P; Au J; Havelock J
Med Image Anal; 2020 May; 62():101612. PubMed ID: 32120267
[TBL] [Abstract][Full Text] [Related]
13. NDG-CAM: Nuclei Detection in Histopathology Images with Semantic Segmentation Networks and Grad-CAM.
Altini N; Brunetti A; Puro E; Taccogna MG; Saponaro C; Zito FA; De Summa S; Bevilacqua V
Bioengineering (Basel); 2022 Sep; 9(9):. PubMed ID: 36135021
[TBL] [Abstract][Full Text] [Related]
14. A novel adaptive cubic quasi-Newton optimizer for deep learning based medical image analysis tasks, validated on detection of COVID-19 and segmentation for COVID-19 lung infection, liver tumor, and optic disc/cup.
Liu Y; Zhang M; Zhong Z; Zeng X
Med Phys; 2023 Mar; 50(3):1528-1538. PubMed ID: 36057788
[TBL] [Abstract][Full Text] [Related]
15. Deep structured residual encoder-decoder network with a novel loss function for nuclei segmentation of kidney and breast histopathology images.
Chanchal AK; Lal S; Kini J
Multimed Tools Appl; 2022; 81(7):9201-9224. PubMed ID: 35125928
[TBL] [Abstract][Full Text] [Related]
16. Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases.
Janowczyk A; Madabhushi A
J Pathol Inform; 2016; 7():29. PubMed ID: 27563488
[TBL] [Abstract][Full Text] [Related]
17. Multiscale unsupervised domain adaptation for automatic pancreas segmentation in CT volumes using adversarial learning.
Zhu Y; Hu P; Li X; Tian Y; Bai X; Liang T; Li J
Med Phys; 2022 Sep; 49(9):5799-5818. PubMed ID: 35833617
[TBL] [Abstract][Full Text] [Related]
18. Deep learning in digital pathology image analysis: a survey.
Deng S; Zhang X; Yan W; Chang EI; Fan Y; Lai M; Xu Y
Front Med; 2020 Aug; 14(4):470-487. PubMed ID: 32728875
[TBL] [Abstract][Full Text] [Related]
19. On the usability of synthetic data for improving the robustness of deep learning-based segmentation of cardiac magnetic resonance images.
Al Khalil Y; Amirrajab S; Lorenz C; Weese J; Pluim J; Breeuwer M
Med Image Anal; 2023 Feb; 84():102688. PubMed ID: 36493702
[TBL] [Abstract][Full Text] [Related]
20. Marker-controlled watershed with deep edge emphasis and optimized H-minima transform for automatic segmentation of densely cultivated 3D cell nuclei.
Kaseva T; Omidali B; Hippeläinen E; Mäkelä T; Wilppu U; Sofiev A; Merivaara A; Yliperttula M; Savolainen S; Salli E
BMC Bioinformatics; 2022 Jul; 23(1):289. PubMed ID: 35864453
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
