These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
222 related articles for article (PubMed ID: 30716022)
1. Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map. Naylor P; Lae M; Reyal F; Walter T IEEE Trans Med Imaging; 2019 Feb; 38(2):448-459. PubMed ID: 30716022 [TBL] [Abstract][Full Text] [Related]
2. A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images. Cui Y; Zhang G; Liu Z; Xiong Z; Hu J Med Biol Eng Comput; 2019 Sep; 57(9):2027-2043. PubMed ID: 31346949 [TBL] [Abstract][Full Text] [Related]
3. 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]
4. Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images. Sirinukunwattana K; Ahmed Raza SE; Yee-Wah Tsang ; Snead DR; Cree IA; Rajpoot NM IEEE Trans Med Imaging; 2016 May; 35(5):1196-1206. PubMed ID: 26863654 [TBL] [Abstract][Full Text] [Related]
5. 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]
6. MDC-net: A new convolutional neural network for nucleus segmentation in histopathology images with distance maps and contour information. Liu X; Guo Z; Cao J; Tang J Comput Biol Med; 2021 Aug; 135():104543. PubMed ID: 34146800 [TBL] [Abstract][Full Text] [Related]
7. 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]
8. Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: A deep learning approach. Van Eycke YR; Balsat C; Verset L; Debeir O; Salmon I; Decaestecker C Med Image Anal; 2018 Oct; 49():35-45. PubMed ID: 30081241 [TBL] [Abstract][Full Text] [Related]
9. Pixel-to-Pixel Learning With Weak Supervision for Single-Stage Nucleus Recognition in Ki67 Images. Xing F; Cornish TC; Bennett T; Ghosh D; Yang L IEEE Trans Biomed Eng; 2019 Nov; 66(11):3088-3097. PubMed ID: 30802845 [TBL] [Abstract][Full Text] [Related]
10. Deep learning nuclei detection: A simple approach can deliver state-of-the-art results. Höfener H; Homeyer A; Weiss N; Molin J; Lundström CF; Hahn HK Comput Med Imaging Graph; 2018 Dec; 70():43-52. PubMed ID: 30286333 [TBL] [Abstract][Full Text] [Related]
11. An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation. Hoseini F; Shahbahrami A; Bayat P J Digit Imaging; 2018 Oct; 31(5):738-747. PubMed ID: 29488179 [TBL] [Abstract][Full Text] [Related]
12. Impact of pre-analytical variables on deep learning accuracy in histopathology. Jones AD; Graff JP; Darrow M; Borowsky A; Olson KA; Gandour-Edwards R; Datta Mitra A; Wei D; Gao G; Durbin-Johnson B; Rashidi HH Histopathology; 2019 Jul; 75(1):39-53. PubMed ID: 30801768 [TBL] [Abstract][Full Text] [Related]
13. Image quilting and wavelet fusion for creation of synthetic microscopy nuclei images. Glotsos D; Kostopoulos S; Ravazoula P; Cavouras D Comput Methods Programs Biomed; 2018 Aug; 162():177-186. PubMed ID: 29903484 [TBL] [Abstract][Full Text] [Related]
14. Patch-level Tumor Classification in Digital Histopathology Images with Domain Adapted Deep Learning. Xia T; Kumar A; Feng D; Kim J Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():644-647. PubMed ID: 30440479 [TBL] [Abstract][Full Text] [Related]
15. A Cascaded Deep Learning Framework for Segmentation of Nuclei in Digital Histology Images. Saednia K; Tran WT; Sadeghi-Naini A Annu Int Conf IEEE Eng Med Biol Soc; 2022 Jul; 2022():4764-4767. PubMed ID: 36086360 [TBL] [Abstract][Full Text] [Related]
16. Improving Dermoscopic Image Segmentation with Enhanced Convolutional-Deconvolutional Networks. Yuan Y; Lo YC IEEE J Biomed Health Inform; 2019 Mar; 23(2):519-526. PubMed ID: 29990146 [TBL] [Abstract][Full Text] [Related]
17. Segmentation of Overlapping Cervical Cells with Mask Region Convolutional Neural Network. Chen J; Zhang B Comput Math Methods Med; 2021; 2021():3890988. PubMed ID: 34646333 [TBL] [Abstract][Full Text] [Related]
18. A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks. Lagree A; Mohebpour M; Meti N; Saednia K; Lu FI; Slodkowska E; Gandhi S; Rakovitch E; Shenfield A; Sadeghi-Naini A; Tran WT Sci Rep; 2021 Apr; 11(1):8025. PubMed ID: 33850222 [TBL] [Abstract][Full Text] [Related]
19. DCAN: Deep contour-aware networks for object instance segmentation from histology images. Chen H; Qi X; Yu L; Dou Q; Qin J; Heng PA Med Image Anal; 2017 Feb; 36():135-146. PubMed ID: 27898306 [TBL] [Abstract][Full Text] [Related]
20. Contour-Seed Pairs Learning-Based Framework for Simultaneously Detecting and Segmenting Various Overlapping Cells/Nuclei in Microscopy Images. Song J; Xiao L; Lian Z IEEE Trans Image Process; 2018 Dec; 27(12):5759-5774. PubMed ID: 30028701 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]