Pages that link to "Item:Q2042989"
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The following pages link to Breast cancer nuclei segmentation and classification based on a deep learning approach (Q2042989):
Displaying 19 items.
- A novel hepatocellular carcinoma image classification method based on voting ranking random forests (Q332979) (← links)
- Breast cancer mitotic cell detection using cascade convolutional neural network with U-net (Q1980107) (← links)
- Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network (Q1982424) (← links)
- Design and analysis of a robust breast cancer diagnostic system based on multimode MR images (Q1984103) (← links)
- Automatic segmentation of pathological glomerular basement membrane in transmission electron microscopy images with random forest stacks (Q2003637) (← links)
- Direct cellularity estimation on breast cancer histopathology images using transfer learning (Q2003647) (← links)
- Bounded-abstaining classification for breast tumors in imbalanced ultrasound images (Q2023639) (← links)
- Automatic extraction of cell nuclei using dilated convolutional network (Q2028918) (← links)
- A hybrid two-stage squeezenet and support vector machine system for Parkinson's disease detection based on handwritten spiral patterns (Q2115906) (← links)
- Multistage classification of oral histopathological images using improved residual network (Q2130251) (← links)
- Automatic detection of breast cancer mitotic cells based on the combination of textural, statistical and innovative mathematical features (Q2285322) (← links)
- Nuclei segmentation for computer-aided diagnosis of breast cancer (Q2509446) (← links)
- Chromenet: a CNN architecture with comparison of optimizers for classification of human chromosome images (Q2675467) (← links)
- Segmentation of Breast Cancer Fine Needle Biopsy Cytological Images (Q3171673) (← links)
- Skin Lesion Segmentation Using Local Binary Convolution-Deconvolution Architecture (Q3387192) (← links)
- Cascade sparse convolution and decision tree ensemble model for nuclear segmentation in pathology images (Q4998058) (← links)
- Efficient Deep Learning Framework with Group Convolution for Segmentation of Histopathology Image (Q5050282) (← links)
- Constant Q-transform-based deep learning architecture for detection of obstructive sleep apnea (Q6069363) (← links)
- Assessment measures of an ensemble classifier based on the distributivity equation to predict the presence of severe coronary artery disease (Q6090242) (← links)
