MAX-Breast-Cancer-Mitosis-Detector
Mitosis detector
A machine learning-based system for detecting mitosis in breast cancer tumor cells from images
Detect whether a mitosis exists in an image of breast cancer tumor cells
23 stars
25 watching
25 forks
Language: Python
last commit: almost 2 years ago
Linked from 1 awesome list
docker-imagekerasmachine-learningmachine-learning-models
Related projects:
Repository | Description | Stars |
---|---|---|
| A tool for detecting objects in images using deep learning models | 290 |
| Develops machine learning models to detect breast cancer from mammography images using deep learning techniques | 378 |
| A Python-based deep learning model for breast cancer classification from mammography images | 849 |
| Detects and outlines tumor areas in MRI images using image processing and segmentation techniques. | 57 |
| A machine learning project to classify brain tumor images into malignant and benign using image processing techniques | 40 |
| An image classification model using the ResNet-50 architecture, trained on the ImageNet dataset. | 14 |
| Detects sentiment in short pieces of text using a pre-trained BERT model fine-tuned on the IBM Claims Dataset. | 58 |
| Develops an automatic prediction model for breast cancer proliferation scores from whole-slide histopathology images using deep learning techniques. | 207 |
| An image classification model using a third-generation deep residual network. | 27 |
| A Matlab implementation of simultaneous object detection and segmentation using deep learning techniques. | 96 |
| Detects humans and estimates their poses in images using a machine learning model based on OpenPose | 65 |
| Classifies high-resolution microscopy images of lung adenocarcinoma using deep neural networks with a sliding window framework. | 494 |
| An image classification model for recognizing physical places and locations | 41 |
| Develops a system to detect, segment, and rank camouflaged objects in images. | 74 |
| Detects cancer metastasis in whole slide images using deep learning and conditional random fields | 757 |