Name | DL | Torrents | Total Size | Joe's Recommended Mirror List [edit] | 233 | 8.28TB | 2028 | 0 | Computer Vision [edit] | 79 | 1.41TB | 610 | 0 | Medical [edit] | 87 | 2.20TB | 794 | 0 | New Collection [edit] | 18 | 5.64TB | 147 | 0 |
BreastCancerCell_dataset (174 files)
ytma10_010704_benign1.TIF | 689.06kB |
ytma10_010704_benign1.TIF.xml | 0.37kB |
ytma10_010704_benign1_ccd.tif | 2.08MB |
ytma10_010704_benign2.TIF | 689.06kB |
ytma10_010704_benign2.TIF.xml | 0.37kB |
ytma10_010704_benign2_ccd.tif | 2.08MB |
ytma10_010704_benign3.TIF | 689.06kB |
ytma10_010704_benign3.TIF.xml | 0.37kB |
ytma10_010704_benign3_ccd.tif | 2.08MB |
ytma10_010704_malignant1.TIF | 689.06kB |
ytma10_010704_malignant1.TIF.xml | 0.38kB |
ytma10_010704_malignant1_ccd.tif | 2.08MB |
ytma10_010704_malignant2.TIF | 689.06kB |
ytma10_010704_malignant2.TIF.xml | 0.38kB |
ytma10_010704_malignant2_ccd.tif | 2.08MB |
ytma10_010704_malignant3.TIF | 689.06kB |
ytma10_010704_malignant3.TIF.xml | 0.38kB |
ytma10_010704_malignant3_ccd.tif | 2.08MB |
ytma12_010804_benign1.TIF | 689.06kB |
ytma12_010804_benign1.TIF.xml | 0.37kB |
ytma12_010804_benign1_ccd.tif | 2.07MB |
ytma12_010804_benign2.TIF | 689.06kB |
ytma12_010804_benign2.TIF.xml | 0.37kB |
ytma12_010804_benign2_ccd.tif | 2.07MB |
ytma12_010804_benign3.TIF | 689.06kB |
ytma12_010804_benign3.TIF.xml | 0.37kB |
ytma12_010804_benign3_ccd.tif | 2.07MB |
ytma12_010804_malignant1.TIF | 689.06kB |
ytma12_010804_malignant1.TIF.xml | 0.38kB |
ytma12_010804_malignant1_ccd.tif | 2.07MB |
ytma12_010804_malignant2.TIF | 689.06kB |
ytma12_010804_malignant2.TIF.xml | 0.38kB |
ytma12_010804_malignant2_ccd.tif | 2.06MB |
ytma12_010804_malignant3.TIF | 689.06kB |
ytma12_010804_malignant3.TIF.xml | 0.38kB |
ytma12_010804_malignant3_ccd.tif | 2.05MB |
ytma23_022103_benign1.TIF | 689.06kB |
ytma23_022103_benign1.TIF.xml | 0.37kB |
ytma23_022103_benign1_ccd.tif | 2.07MB |
ytma23_022103_benign2.TIF | 689.06kB |
ytma23_022103_benign2.TIF.xml | 0.37kB |
ytma23_022103_benign2_ccd.tif | 2.07MB |
ytma23_022103_benign3.TIF | 689.06kB |
ytma23_022103_benign3.TIF.xml | 0.37kB |
ytma23_022103_benign3_ccd.tif | 2.02MB |
ytma23_022103_malignant1.TIF | 689.06kB |
ytma23_022103_malignant1.TIF.xml | 0.38kB |
ytma23_022103_malignant1_ccd.tif | 2.08MB |
ytma23_022103_malignant2.TIF | 689.06kB |
Type: Dataset
Tags:
Bibtex:
Tags:
Bibtex:
@article{, title= {Breast Cancer Cell Segmentation}, keywords= {}, author= {Elisa Drelie Gelasca and Jiyun Byun and Boguslaw Obara and B.S. Manjunath}, abstract= {There are about 58 H&E stained histopathology images used in breast cancer cell detection with associated ground truth data available. Routine histology uses the stain combination of hematoxylin and eosin, commonly referred to as H&E. These images are stained since most cells are essentially transparent, with little or no intrinsic pigment. Certain special stains, which bind selectively to particular components, are be used to identify biological structures such as cells. In those images, the challenging problem is cell segmentation for subsequent classification in benign and malignant cells. The ground truth have been obtained for one image containing benign cells. | Image: |Ground Truth: | |---|---| | ![](https://i.imgur.com/haa5X8O.png) | ![](https://i.imgur.com/gqBikTa.png) | All images: ![](https://i.imgur.com/QM22bG2.png)}, terms= {}, license= {}, superseded= {}, url= {http://bioimage.ucsb.edu/research/bio-segmentation} }