257 related articles for article (PubMed ID: 25547073)
1. Assessment of algorithms for mitosis detection in breast cancer histopathology images.
Veta M; van Diest PJ; Willems SM; Wang H; Madabhushi A; Cruz-Roa A; Gonzalez F; Larsen AB; Vestergaard JS; Dahl AB; Cireşan DC; Schmidhuber J; Giusti A; Gambardella LM; Tek FB; Walter T; Wang CW; Kondo S; Matuszewski BJ; Precioso F; Snell V; Kittler J; de Campos TE; Khan AM; Rajpoot NM; Arkoumani E; Lacle MM; Viergever MA; Pluim JP
Med Image Anal; 2015 Feb; 20(1):237-48. PubMed ID: 25547073
[TBL] [Abstract][Full Text] [Related]
2. Mitosis Counting in Breast Cancer: Object-Level Interobserver Agreement and Comparison to an Automatic Method.
Veta M; van Diest PJ; Jiwa M; Al-Janabi S; Pluim JP
PLoS One; 2016; 11(8):e0161286. PubMed ID: 27529701
[TBL] [Abstract][Full Text] [Related]
3. Towards semantic-driven high-content image analysis: an operational instantiation for mitosis detection in digital histopathology.
Racoceanu D; Capron F
Comput Med Imaging Graph; 2015 Jun; 42():2-15. PubMed ID: 25442055
[TBL] [Abstract][Full Text] [Related]
4. Mitosis detection in breast cancer histological images An ICPR 2012 contest.
Roux L; Racoceanu D; Loménie N; Kulikova M; Irshad H; Klossa J; Capron F; Genestie C; Le Naour G; Gurcan MN
J Pathol Inform; 2013; 4():8. PubMed ID: 23858383
[TBL] [Abstract][Full Text] [Related]
5. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer.
Ehteshami Bejnordi B; Veta M; Johannes van Diest P; van Ginneken B; Karssemeijer N; Litjens G; van der Laak JAWM; ; Hermsen M; Manson QF; Balkenhol M; Geessink O; Stathonikos N; van Dijk MC; Bult P; Beca F; Beck AH; Wang D; Khosla A; Gargeya R; Irshad H; Zhong A; Dou Q; Li Q; Chen H; Lin HJ; Heng PA; Haß C; Bruni E; Wong Q; Halici U; Öner MÜ; Cetin-Atalay R; Berseth M; Khvatkov V; Vylegzhanin A; Kraus O; Shaban M; Rajpoot N; Awan R; Sirinukunwattana K; Qaiser T; Tsang YW; Tellez D; Annuscheit J; Hufnagl P; Valkonen M; Kartasalo K; Latonen L; Ruusuvuori P; Liimatainen K; Albarqouni S; Mungal B; George A; Demirci S; Navab N; Watanabe S; Seno S; Takenaka Y; Matsuda H; Ahmady Phoulady H; Kovalev V; Kalinovsky A; Liauchuk V; Bueno G; Fernandez-Carrobles MM; Serrano I; Deniz O; Racoceanu D; Venâncio R
JAMA; 2017 Dec; 318(22):2199-2210. PubMed ID: 29234806
[TBL] [Abstract][Full Text] [Related]
6. Mitosis detection, fast and slow: Robust and efficient detection of mitotic figures.
Jahanifar M; Shephard A; Zamanitajeddin N; Graham S; Raza SEA; Minhas F; Rajpoot N
Med Image Anal; 2024 May; 94():103132. PubMed ID: 38442527
[TBL] [Abstract][Full Text] [Related]
7. Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses.
Pantanowitz L; Hartman D; Qi Y; Cho EY; Suh B; Paeng K; Dhir R; Michelow P; Hazelhurst S; Song SY; Cho SY
Diagn Pathol; 2020 Jul; 15(1):80. PubMed ID: 32622359
[TBL] [Abstract][Full Text] [Related]
8. Evaluation of mitotic activity index in breast cancer using whole slide digital images.
Al-Janabi S; van Slooten HJ; Visser M; van der Ploeg T; van Diest PJ; Jiwa M
PLoS One; 2013; 8(12):e82576. PubMed ID: 24386102
[TBL] [Abstract][Full Text] [Related]
9. Weakly supervised mitosis detection in breast histopathology images using concentric loss.
Li C; Wang X; Liu W; Latecki LJ; Wang B; Huang J
Med Image Anal; 2019 Apr; 53():165-178. PubMed ID: 30798116
[TBL] [Abstract][Full Text] [Related]
10. Efficient deep learning model for mitosis detection using breast histopathology images.
Saha M; Chakraborty C; Racoceanu D
Comput Med Imaging Graph; 2018 Mar; 64():29-40. PubMed ID: 29409716
[TBL] [Abstract][Full Text] [Related]
11. Mitosis Detection for Invasive Breast Cancer Grading in Histopathological Images.
Paul A; Mukherjee DP
IEEE Trans Image Process; 2015 Nov; 24(11):4041-54. PubMed ID: 26219094
[TBL] [Abstract][Full Text] [Related]
12. Multispectral band selection and spatial characterization: Application to mitosis detection in breast cancer histopathology.
Irshad H; Gouaillard A; Roux L; Racoceanu D
Comput Med Imaging Graph; 2014 Jul; 38(5):390-402. PubMed ID: 24831181
[TBL] [Abstract][Full Text] [Related]
13. Uninformed Teacher-Student for hard-samples distillation in weakly supervised mitosis localization.
Fernandez-Martín C; Silva-Rodriguez J; Kiraz U; Morales S; Janssen EAM; Naranjo V
Comput Med Imaging Graph; 2024 Mar; 112():102328. PubMed ID: 38244279
[TBL] [Abstract][Full Text] [Related]
14. Mitotic figure recognition: agreement among pathologists and computerized detector.
Malon C; Brachtel E; Cosatto E; Graf HP; Kurata A; Kuroda M; Meyer JS; Saito A; Wu S; Yagi Y
Anal Cell Pathol (Amst); 2012; 35(2):97-100. PubMed ID: 21965283
[TBL] [Abstract][Full Text] [Related]
15. Evaluation of the interobserver agreement in the number of mitotic figures of breast carcinoma as simulation of quality monitoring in the Japan National Surgical Adjuvant Study of Breast Cancer (NSAS-BC) protocol.
Tsuda H; Akiyama F; Kurosumi M; Sakamoto G; Yamashiro K; Oyama T; Hasebe T; Kameyama K; Hasegawa T; Umemura S; Honma K; Ozawa T; Sasaki K; Morino H; Ohsumi S
Jpn J Cancer Res; 2000 Apr; 91(4):451-7. PubMed ID: 10804295
[TBL] [Abstract][Full Text] [Related]
16. Visual assessment of mitotic figures in breast cancer: a comparative study between light microscopy and whole slide images.
Lashen A; Ibrahim A; Katayama A; Ball G; Mihai R; Toss M; Rakha E
Histopathology; 2021 Dec; 79(6):913-925. PubMed ID: 34455620
[TBL] [Abstract][Full Text] [Related]
17. Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks.
Tellez D; Balkenhol M; Otte-Holler I; van de Loo R; Vogels R; Bult P; Wauters C; Vreuls W; Mol S; Karssemeijer N; Litjens G; van der Laak J; Ciompi F
IEEE Trans Med Imaging; 2018 Sep; 37(9):2126-2136. PubMed ID: 29994086
[TBL] [Abstract][Full Text] [Related]
18. A deep learning approach for mitosis detection: Application in tumor proliferation prediction from whole slide images.
Nateghi R; Danyali H; Helfroush MS
Artif Intell Med; 2021 Apr; 114():102048. PubMed ID: 33875159
[TBL] [Abstract][Full Text] [Related]
19. Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review.
Chen JM; Li Y; Xu J; Gong L; Wang LW; Liu WL; Liu J
Tumour Biol; 2017 Mar; 39(3):1010428317694550. PubMed ID: 28347240
[TBL] [Abstract][Full Text] [Related]
20. Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge.
Veta M; Heng YJ; Stathonikos N; Bejnordi BE; Beca F; Wollmann T; Rohr K; Shah MA; Wang D; Rousson M; Hedlund M; Tellez D; Ciompi F; Zerhouni E; Lanyi D; Viana M; Kovalev V; Liauchuk V; Phoulady HA; Qaiser T; Graham S; Rajpoot N; Sjöblom E; Molin J; Paeng K; Hwang S; Park S; Jia Z; Chang EI; Xu Y; Beck AH; van Diest PJ; Pluim JPW
Med Image Anal; 2019 May; 54():111-121. PubMed ID: 30861443
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]