BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

382 related articles for article (PubMed ID: 35671603)

  • 1. StoHisNet: A hybrid multi-classification model with CNN and Transformer for gastric pathology images.
    Fu B; Zhang M; He J; Cao Y; Guo Y; Wang R
    Comput Methods Programs Biomed; 2022 Jun; 221():106924. PubMed ID: 35671603
    [TBL] [Abstract][Full Text] [Related]  

  • 2. A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images.
    Dabass M; Vashisth S; Vig R
    Comput Biol Med; 2022 Aug; 147():105680. PubMed ID: 35671654
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging.
    Ueyama H; Kato Y; Akazawa Y; Yatagai N; Komori H; Takeda T; Matsumoto K; Ueda K; Matsumoto K; Hojo M; Yao T; Nagahara A; Tada T
    J Gastroenterol Hepatol; 2021 Feb; 36(2):482-489. PubMed ID: 32681536
    [TBL] [Abstract][Full Text] [Related]  

  • 4. DAX-Net: A dual-branch dual-task adaptive cross-weight feature fusion network for robust multi-class cancer classification in pathology images.
    Bui DC; Song B; Kim K; Kwak JT
    Comput Methods Programs Biomed; 2024 May; 248():108112. PubMed ID: 38479146
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Human-computer interaction based health diagnostics using ResNet34 for tongue image classification.
    Zhuang Q; Gan S; Zhang L
    Comput Methods Programs Biomed; 2022 Nov; 226():107096. PubMed ID: 36191350
    [TBL] [Abstract][Full Text] [Related]  

  • 6. An improved transformer network for skin cancer classification.
    Xin C; Liu Z; Zhao K; Miao L; Ma Y; Zhu X; Zhou Q; Wang S; Li L; Yang F; Xu S; Chen H
    Comput Biol Med; 2022 Oct; 149():105939. PubMed ID: 36037629
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Transformer-based multi-task learning for classification and segmentation of gastrointestinal tract endoscopic images.
    Tang S; Yu X; Cheang CF; Liang Y; Zhao P; Yu HH; Choi IC
    Comput Biol Med; 2023 May; 157():106723. PubMed ID: 36907035
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Detecting early gastric cancer: Comparison between the diagnostic ability of convolutional neural networks and endoscopists.
    Ikenoyama Y; Hirasawa T; Ishioka M; Namikawa K; Yoshimizu S; Horiuchi Y; Ishiyama A; Yoshio T; Tsuchida T; Takeuchi Y; Shichijo S; Katayama N; Fujisaki J; Tada T
    Dig Endosc; 2021 Jan; 33(1):141-150. PubMed ID: 32282110
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Automated classification of gastric neoplasms in endoscopic images using a convolutional neural network.
    Cho BJ; Bang CS; Park SW; Yang YJ; Seo SI; Lim H; Shin WG; Hong JT; Yoo YT; Hong SH; Choi JH; Lee JJ; Baik GH
    Endoscopy; 2019 Dec; 51(12):1121-1129. PubMed ID: 31443108
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Transformaer-based model for lung adenocarcinoma subtypes.
    Du F; Zhou H; Niu Y; Han Z; Sui X
    Med Phys; 2024 Mar; ():. PubMed ID: 38427790
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Land Cover Classification of UAV Remote Sensing Based on Transformer-CNN Hybrid Architecture.
    Lu T; Wan L; Qi S; Gao M
    Sensors (Basel); 2023 Jun; 23(11):. PubMed ID: 37300015
    [TBL] [Abstract][Full Text] [Related]  

  • 12. GasHisSDB: A new gastric histopathology image dataset for computer aided diagnosis of gastric cancer.
    Hu W; Li C; Li X; Rahaman MM; Ma J; Zhang Y; Chen H; Liu W; Sun C; Yao Y; Sun H; Grzegorzek M
    Comput Biol Med; 2022 Mar; 142():105207. PubMed ID: 35016101
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A VGG attention vision transformer network for benign and malignant classification of breast ultrasound images.
    Qu X; Lu H; Tang W; Wang S; Zheng D; Hou Y; Jiang J
    Med Phys; 2022 Sep; 49(9):5787-5798. PubMed ID: 35866492
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Deep-Hipo: Multi-scale receptive field deep learning for histopathological image analysis.
    Kosaraju SC; Hao J; Koh HM; Kang M
    Methods; 2020 Jul; 179():3-13. PubMed ID: 32442672
    [TBL] [Abstract][Full Text] [Related]  

  • 15. BCHisto-Net: Breast histopathological image classification by global and local feature aggregation.
    R R; Prasad K; Udupa CBK
    Artif Intell Med; 2021 Nov; 121():102191. PubMed ID: 34763806
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Global hybrid multi-scale convolutional network for accurate and robust detection of atrial fibrillation using single-lead ECG recordings.
    Zhang P; Ma C; Sun Y; Fan G; Song F; Feng Y; Zhang G
    Comput Biol Med; 2021 Dec; 139():104880. PubMed ID: 34700255
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Towards more efficient ophthalmic disease classification and lesion location via convolution transformer.
    Wen H; Zhao J; Xiang S; Lin L; Liu C; Wang T; An L; Liang L; Huang B
    Comput Methods Programs Biomed; 2022 Jun; 220():106832. PubMed ID: 35525213
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A dual data stream hybrid neural network for classifying pathological images of lung adenocarcinoma.
    Li L; Mei Z; Li Y; Yu Y; Liu M
    Comput Biol Med; 2024 Jun; 175():108519. PubMed ID: 38688128
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Abnormality classification and localization using dual-branch whole-region-based CNN model with histopathological images.
    Oyelade ON; Ezugwu AE; Venter HS; Mirjalili S; Gandomi AH
    Comput Biol Med; 2022 Oct; 149():105943. PubMed ID: 35986967
    [TBL] [Abstract][Full Text] [Related]  

  • 20. LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images.
    Aatresh AA; Alabhya K; Lal S; Kini J; Saxena PUP
    Int J Comput Assist Radiol Surg; 2021 Sep; 16(9):1549-1563. PubMed ID: 34053009
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 20.