119 related articles for article (PubMed ID: 37660106)
1. A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification.
Wang D; Wang X; Wang L; Li M; Da Q; Liu X; Gao X; Shen J; He J; Shen T; Duan Q; Zhao J; Li K; Qiao Y; Zhang S
Sci Data; 2023 Sep; 10(1):574. PubMed ID: 37660106
[TBL] [Abstract][Full Text] [Related]
2. TEM virus images: Benchmark dataset and deep learning classification.
Matuszewski DJ; Sintorn IM
Comput Methods Programs Biomed; 2021 Sep; 209():106318. PubMed ID: 34375851
[TBL] [Abstract][Full Text] [Related]
3. A Benchmark for Studying Diabetic Retinopathy: Segmentation, Grading, and Transferability.
Zhou Y; Wang B; Huang L; Cui S; Shao L
IEEE Trans Med Imaging; 2021 Mar; 40(3):818-828. PubMed ID: 33180722
[TBL] [Abstract][Full Text] [Related]
4. Retinal images benchmark for the detection of diabetic retinopathy and clinically significant macular edema (CSME).
Noor-Ul-Huda M; Tehsin S; Ahmed S; Niazi FAK; Murtaza Z
Biomed Tech (Berl); 2019 May; 64(3):297-307. PubMed ID: 30055096
[TBL] [Abstract][Full Text] [Related]
5. Deep Transfer Learning with Enhanced Feature Fusion for Detection of Abnormalities in X-ray Images.
Alammar Z; Alzubaidi L; Zhang J; Li Y; Lafta W; Gu Y
Cancers (Basel); 2023 Aug; 15(15):. PubMed ID: 37568821
[TBL] [Abstract][Full Text] [Related]
6. Contrastive pre-training and linear interaction attention-based transformer for universal medical reports generation.
Lin Z; Zhang D; Shi D; Xu R; Tao Q; Wu L; He M; Ge Z
J Biomed Inform; 2023 Feb; 138():104281. PubMed ID: 36638935
[TBL] [Abstract][Full Text] [Related]
7. Automated Quality Evaluation of Large-Scale Benchmark Datasets for Vision-Language Tasks.
Zhao R; Xie Z; Zhuang Y; L H Yu P
Int J Neural Syst; 2024 Mar; 34(3):2450009. PubMed ID: 38318751
[TBL] [Abstract][Full Text] [Related]
8. IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge.
Porwal P; Pachade S; Kokare M; Deshmukh G; Son J; Bae W; Liu L; Wang J; Liu X; Gao L; Wu T; Xiao J; Wang F; Yin B; Wang Y; Danala G; He L; Choi YH; Lee YC; Jung SH; Li Z; Sui X; Wu J; Li X; Zhou T; Toth J; Baran A; Kori A; Chennamsetty SS; Safwan M; Alex V; Lyu X; Cheng L; Chu Q; Li P; Ji X; Zhang S; Shen Y; Dai L; Saha O; Sathish R; Melo T; Araújo T; Harangi B; Sheng B; Fang R; Sheet D; Hajdu A; Zheng Y; Mendonça AM; Zhang S; Campilho A; Zheng B; Shen D; Giancardo L; Quellec G; Mériaudeau F
Med Image Anal; 2020 Jan; 59():101561. PubMed ID: 31671320
[TBL] [Abstract][Full Text] [Related]
9. ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI.
Boone L; Biparva M; Mojiri Forooshani P; Ramirez J; Masellis M; Bartha R; Symons S; Strother S; Black SE; Heyn C; Martel AL; Swartz RH; Goubran M
Neuroimage; 2023 Sep; 278():120289. PubMed ID: 37495197
[TBL] [Abstract][Full Text] [Related]
10. A benchmark bone marrow aspirate smear dataset and a multi-scale cell detection model for the diagnosis of hematological disorders.
Su J; Han J; Song J
Comput Med Imaging Graph; 2021 Jun; 90():101912. PubMed ID: 33892388
[TBL] [Abstract][Full Text] [Related]
11. Deep image mining for diabetic retinopathy screening.
Quellec G; Charrière K; Boudi Y; Cochener B; Lamard M
Med Image Anal; 2017 Jul; 39():178-193. PubMed ID: 28511066
[TBL] [Abstract][Full Text] [Related]
12. Weakly supervised training for eye fundus lesion segmentation in patients with diabetic retinopathy.
Li Y; Zhu M; Sun G; Chen J; Zhu X; Yang J
Math Biosci Eng; 2022 Mar; 19(5):5293-5311. PubMed ID: 35430865
[TBL] [Abstract][Full Text] [Related]
13. Tufts Dental Database: A Multimodal Panoramic X-Ray Dataset for Benchmarking Diagnostic Systems.
Panetta K; Rajendran R; Ramesh A; Rao S; Agaian S
IEEE J Biomed Health Inform; 2022 Apr; 26(4):1650-1659. PubMed ID: 34606466
[TBL] [Abstract][Full Text] [Related]
14. Construction of benchmark retinal image database for diabetic retinopathy analysis.
Kaur J; Mittal D
Proc Inst Mech Eng H; 2020 Sep; 234(9):1036-1048. PubMed ID: 32605477
[TBL] [Abstract][Full Text] [Related]
15. Multi-Learner Based Deep Meta-Learning for Few-Shot Medical Image Classification.
Jiang H; Gao M; Li H; Jin R; Miao H; Liu J
IEEE J Biomed Health Inform; 2023 Jan; 27(1):17-28. PubMed ID: 36251917
[TBL] [Abstract][Full Text] [Related]
16. Combining transfer learning with retinal lesion features for accurate detection of diabetic retinopathy.
Hassan D; Gill HM; Happe M; Bhatwadekar AD; Hajrasouliha AR; Janga SC
Front Med (Lausanne); 2022; 9():1050436. PubMed ID: 36425113
[TBL] [Abstract][Full Text] [Related]
17. A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis.
Hosseinzadeh Taher MR; Haghighi F; Feng R; Gotway MB; Liang J
Domain Adapt Represent Transf Afford Healthc AI Resour Divers Glob Health (2021); 2021; 12968():3-13. PubMed ID: 35713581
[TBL] [Abstract][Full Text] [Related]
18. Does your dermatology classifier know what it doesn't know? Detecting the long-tail of unseen conditions.
Guha Roy A; Ren J; Azizi S; Loh A; Natarajan V; Mustafa B; Pawlowski N; Freyberg J; Liu Y; Beaver Z; Vo N; Bui P; Winter S; MacWilliams P; Corrado GS; Telang U; Liu Y; Cemgil T; Karthikesalingam A; Lakshminarayanan B; Winkens J
Med Image Anal; 2022 Jan; 75():102274. PubMed ID: 34731777
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Detection of retinopathy disease using morphological gradient and segmentation approaches in fundus images.
Toğaçar M
Comput Methods Programs Biomed; 2022 Feb; 214():106579. PubMed ID: 34896689
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]