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Kaggle Inc r cnn
R Cnn, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/r cnn/product/Kaggle Inc
Average 86 stars, based on 1 article reviews
r cnn - by Bioz Stars, 2026-06
86/100 stars

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Correct mitosis detection on a TUPAC16 breast cancer histopathology image. The left panel shows the context view with black boxes marking regions of interest; the right panels show magnified views where green boxes/labels denote mitotic cells automatically detected by the <t>Faster</t> <t>R-CNN–based</t> method
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Correct mitosis detection on a TUPAC16 breast cancer histopathology image. The left panel shows the context view with black boxes marking regions of interest; the right panels show magnified views where green boxes/labels denote mitotic cells automatically detected by the <t>Faster</t> <t>R-CNN–based</t> method
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Correct mitosis detection on a TUPAC16 breast cancer histopathology image. The left panel shows the context view with black boxes marking regions of interest; the right panels show magnified views where green boxes/labels denote mitotic cells automatically detected by the <t>Faster</t> <t>R-CNN–based</t> method
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Correct mitosis detection on a TUPAC16 breast cancer histopathology image. The left panel shows the context view with black boxes marking regions of interest; the right panels show magnified views where green boxes/labels denote mitotic cells automatically detected by the <t>Faster</t> <t>R-CNN–based</t> method
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Correct mitosis detection on a TUPAC16 breast cancer histopathology image. The left panel shows the context view with black boxes marking regions of interest; the right panels show magnified views where green boxes/labels denote mitotic cells automatically detected by the <t>Faster</t> <t>R-CNN–based</t> method
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Correct mitosis detection on a TUPAC16 breast cancer histopathology image. The left panel shows the context view with black boxes marking regions of interest; the right panels show magnified views where green boxes/labels denote mitotic cells automatically detected by the <t>Faster</t> <t>R-CNN–based</t> method
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Correct mitosis detection on a TUPAC16 breast cancer histopathology image. The left panel shows the context view with black boxes marking regions of interest; the right panels show magnified views where green boxes/labels denote mitotic cells automatically detected by the <t>Faster</t> <t>R-CNN–based</t> method
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Correct mitosis detection on a TUPAC16 breast cancer histopathology image. The left panel shows the context view with black boxes marking regions of interest; the right panels show magnified views where green boxes/labels denote mitotic cells automatically detected by the <t>Faster</t> <t>R-CNN–based</t> method
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Correct mitosis detection on a TUPAC16 breast cancer histopathology image. The left panel shows the context view with black boxes marking regions of interest; the right panels show magnified views where green boxes/labels denote mitotic cells automatically detected by the Faster R-CNN–based method

Journal: BioMedical Engineering OnLine

Article Title: Application of deep learning technology in breast cancer: a systematic review of segmentation, detection, and classification approaches

doi: 10.1186/s12938-025-01502-5

Figure Lengend Snippet: Correct mitosis detection on a TUPAC16 breast cancer histopathology image. The left panel shows the context view with black boxes marking regions of interest; the right panels show magnified views where green boxes/labels denote mitotic cells automatically detected by the Faster R-CNN–based method

Article Snippet: [ ] , 2023 , Kaggle , Untitled , √ , Mask R-CNN and ResNet50 were used for detection and classification , 98.0%Acc、0.970AUC.

Techniques: Histopathology