The Acute Lymphoblastic Leukemia Pytorch Segmentation 2021 is a Pytorch segmentation model which pinpoints exactly where the cancerous cells are within images of peripheral blood samples.
The Acute Lymphoblastic Leukemia Pytorch Segmentation 2021 is a Pytorch segmentation model which pipoints exactly where the cancerous cells are within images of peripheral blood samples.
This project is our first project using Pytorch, and also our first segmentation project.
This project should be used for research purposes only. The purpose of the project is to show the potential of Artificial Intelligence for medical support systems such as diagnostic systems.
Although the model is accurate and shows good results both on paper and in real world testing, it is trained on a small amount of data and needs to be trained on larger datasets to really evaluate it's accuracy. This project is not meant to be an alternative to professional medical diagnosis.
Developers that have contributed to this repository have experience in using Artificial Intelligence for detecting certain types of cancer. They are not doctors, medical or cancer experts.
Please use this system responsibly.
Acute Lymphoblastic Leukemia
Acute lymphoblastic leukemia (ALL), also known as Acute Lymphocytic Leukemia, is a cancer that affects the lymphoid blood cell lineage. It is the most common leukemia in children, and it accounts for 10-20% of acute leukemias in adults. The prognosis for both adult and especially childhood ALL has improved substantially since the 1970s. The 5- year survival is approximately 95% in children. In adults, the 5-year survival varies between 25% and 75%, with more favorable results in younger than in older patients.
For more information about Acute Lymphoblastic Leukemia please visit our Leukemia Information Page
You need to be granted access to use the Acute Lymphoblastic Leukemia Image Database for Image Processing dataset. You can find the application form and information about getting access to the dataset on this page as well as information on how to contribute back to the project here. If you are not able to obtain a copy of the dataset please feel free to try this tutorial on your own dataset, we would be very happy to find additional AML & ALL datasets.