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Machine learning toolkit designed to support data scientists in breast cancer analysis.

Project description

Project generated with PyScaffold PyPI-Server

BCT

Machine learning toolkit designed to support data scientists in breast cancer detection, classification and analysis.

Installation

Install the package with:

pip install breast-cancer-toolkit

Usage as CLI

Convert the DCM files into TIFF format recursively with the same basename.

parallel bct convert {} {.}.tiff ::: **/*.dcm

Usage as library

Models

Remove background with instance segmentation

Model Training data Resolution # of samples seen IoU Accuracy

Data sources

Source Argument Type Notes
image 'image.jpg' str or Path Single image file. Format supported: jgp, png, dcm, tiff
URL 'https://ultralytics.com/images/bus.jpg' str URL to an image.
screenshot 'screen' str Capture a screenshot.
PIL Image.open('im.jpg') PIL.Image HWC format with RGB channels.
OpenCV cv2.imread('im.jpg') np.ndarray HWC format with BGR channels uint8 (0-255).
numpy np.zeros((640,1280,3)) np.ndarray HWC format with BGR channels uint8 (0-255).
torch torch.zeros(16,3,320,640) torch.Tensor BCHW format with RGB channels float32 (0.0-1.0).
CSV, json, xlsx 'sources.csv' str or Path CSV file containing paths to images, videos, or directories.
video 'video.mp4' str or Path Video file in formats like MP4, AVI, etc.
directory 'path/' str or Path Path to a directory containing images or videos.
glob 'path/*.jpg' str Glob pattern to match multiple files. Use the * character as a wildcard.
YouTube 'https://youtu.be/LNwODJXcvt4' str URL to a YouTube video.
stream 'rtsp://example.com/media.mp4' str URL for streaming protocols such as RTSP, RTMP, TCP, or an IP address.
multi-stream 'list.streams' str or Path *.streams text file with one stream URL per row, i.e. 8 streams will run at batch-size 8.

Note

This project has been set up using PyScaffold 4.5 and the dsproject extension 0.0.post1.dev166+g2aaddf7.d20240514.

Project details


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