LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 4 of total 4

Search options

  1. Article ; Online: Stain normalization using score-based diffusion model through stain separation and overlapped moving window patch strategies.

    Jeong, Jiheon / Kim, Ki Duk / Nam, Yujin / Cho, Cristina Eunbee / Go, Heounjeong / Kim, Namkug

    Computers in biology and medicine

    2022  Volume 152, Page(s) 106335

    Abstract: Hematoxylin and eosin (H&E) staining is the gold standard modality for diagnosis in medicine. However, the dosage ratio of hematoxylin to eosin in H&E staining has not been standardized yet. Additionally, H&E stains fade out at various speeds. Therefore, ...

    Abstract Hematoxylin and eosin (H&E) staining is the gold standard modality for diagnosis in medicine. However, the dosage ratio of hematoxylin to eosin in H&E staining has not been standardized yet. Additionally, H&E stains fade out at various speeds. Therefore, the staining quality could differ among each image, and stain normalization is a critical preprocessing approach for training deep learning (DL) models, especially in long-term and/or multicenter digital pathology studies. However, conventional methods for stain normalization have some significant drawbacks, such as collapsing in the structure and/or texture of tissue. In addition, conventional methods must require a reference patch or slide. Meanwhile, DL-based methods have a risk of overfitting and/or grid artifacts. We developed a score-based diffusion model of colorization for stain normalization. However, mistransfer, in which the model confuses hematoxylin with eosin, can occur using a score-based diffusion model due to its high diversity nature. To overcome this mistransfer, we propose a stain separation method using sparse non-negative matrix factorization (SNMF), which can decompose pathology slide into Hematoxylin and Eosin to normalize each stain component. Furthermore, inpainting with overlapped moving window patches was used to prevent grid artifacts of whole slide image normalization. Our method can normalize the whole slide pathology images through this stain normalization pipeline with decent performance.
    MeSH term(s) Coloring Agents/chemistry ; Hematoxylin ; Eosine Yellowish-(YS) ; Staining and Labeling ; Algorithms
    Chemical Substances Coloring Agents ; Hematoxylin (YKM8PY2Z55) ; Eosine Yellowish-(YS) (TDQ283MPCW)
    Language English
    Publishing date 2022-11-28
    Publishing country United States
    Document type Multicenter Study ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 127557-4
    ISSN 1879-0534 ; 0010-4825
    ISSN (online) 1879-0534
    ISSN 0010-4825
    DOI 10.1016/j.compbiomed.2022.106335
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 653: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  2. Article ; Online: Characteristics of gastric cancer around the world.

    López, María J / Carbajal, Junior / Alfaro, Alejandro L / Saravia, Luis G / Zanabria, Daniel / Araujo, Jhajaira M / Quispe, Lidia / Zevallos, Alejandra / Buleje, José L / Cho, Cristina Eunbee / Sarmiento, Marisol / Pinto, Joseph A / Fajardo, Williams

    Critical reviews in oncology/hematology

    2022  Volume 181, Page(s) 103841

    Abstract: Gastric cancer is one of the most important malignancies in the world due to the high burden of disease and lethality. In this work, we compared the main characteristics of gastric cancer between different regions of the world. We reviewed public ... ...

    Abstract Gastric cancer is one of the most important malignancies in the world due to the high burden of disease and lethality. In this work, we compared the main characteristics of gastric cancer between different regions of the world. We reviewed public repositories to retrieve epidemiological, molecular, clinicopathological, and risk factor data. Eastern Asia presents the highest incidence of gastric cancer, followed by eastern and central Europe. Intestinal histology was more frequent in Caucasians, while gastric tumors located in the cardias were less frequent in Africa and Latin America. TP53, LRP1B, and ARID1A are consistently the most frequently altered genes in all population groups. Gastric cancer is most frequent in men. African patients tend to be younger and have a higher proportion of women patients. Different patterns can be observed in the presentation of gastric cancer between different regions of the world. More research is needed in Latin America and Africa since these populations are underrepresented.
    Language English
    Publishing date 2022-10-11
    Publishing country Netherlands
    Document type Journal Article ; Review
    ZDB-ID 605680-5
    ISSN 1879-0461 ; 0737-9587 ; 1040-8428
    ISSN (online) 1879-0461
    ISSN 0737-9587 ; 1040-8428
    DOI 10.1016/j.critrevonc.2022.103841
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 1931: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  3. Article ; Online: Deep learning model to predict Epstein-Barr virus associated gastric cancer in histology.

    Jeong, Yeojin / Cho, Cristina Eunbee / Kim, Ji-Eon / Lee, Jonghyun / Kim, Namkug / Jung, Woon Yong / Sung, Joohon / Kim, Ju Han / Lee, Yoo Jin / Jung, Jiyoon / Pyo, Juyeon / Song, Jisun / Park, Jihwan / Moon, Kyoung Min / Ahn, Sangjeong

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 18466

    Abstract: The detection of Epstein-Barr virus (EBV) in gastric cancer patients is crucial for clinical decision making, as it is related with specific treatment responses and prognoses. Despite its importance, the limited medical resources preclude universal EBV ... ...

    Abstract The detection of Epstein-Barr virus (EBV) in gastric cancer patients is crucial for clinical decision making, as it is related with specific treatment responses and prognoses. Despite its importance, the limited medical resources preclude universal EBV testing. Herein, we propose a deep learning-based EBV prediction method from H&E-stained whole-slide images (WSI). Our model was developed using 319 H&E stained WSI (26 EBV positive; TCGA dataset) from the Cancer Genome Atlas, and 108 WSI (8 EBV positive; ISH dataset) from an independent institution. Our deep learning model, EBVNet consists of two sequential components: a tumor classifier and an EBV classifier. We visualized the learned representation by the classifiers using UMAP. We externally validated the model using 60 additional WSI (7 being EBV positive; HGH dataset). We compared the model's performance with those of four pathologists. EBVNet achieved an AUPRC of 0.65, whereas the four pathologists yielded a mean AUPRC of 0.41. Moreover, EBVNet achieved an negative predictive value, sensitivity, specificity, precision, and F1-score of 0.98, 0.86, 0.92, 0.60, and 0.71, respectively. Our proposed model is expected to contribute to prescreen patients for confirmatory testing, potentially to save test-related cost and labor.
    MeSH term(s) Humans ; Herpesvirus 4, Human/genetics ; Stomach Neoplasms/pathology ; Epstein-Barr Virus Infections/genetics ; Deep Learning ; Prognosis
    Language English
    Publishing date 2022-11-02
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-22731-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  4. Article ; Online: Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections.

    Kim, Young-Gon / Kim, Sungchul / Cho, Cristina Eunbee / Song, In Hye / Lee, Hee Jin / Ahn, Soomin / Park, So Yeon / Gong, Gyungyub / Kim, Namkug

    Scientific reports

    2020  Volume 10, Issue 1, Page(s) 21899

    Abstract: Fast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time ... ...

    Abstract Fast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.
    MeSH term(s) Adult ; Aged ; Aged, 80 and over ; Female ; Frozen Sections ; Humans ; Image Interpretation, Computer-Assisted ; Lymphatic Metastasis ; Male ; Middle Aged ; Neoplasms/classification ; Neoplasms/pathology ; Neural Networks, Computer ; Retrospective Studies ; Sentinel Lymph Node Biopsy
    Language English
    Publishing date 2020-12-14
    Publishing country England
    Document type Clinical Trial ; Journal Article ; Multicenter Study ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-020-78129-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

To top