How can we improve breast cancer prediction thanks to machine learning?
Breast cancer prediction is a diagnosis tool. Oncologists and medical staff face the challenge of identifying breast cancer as soon as possible. An early diagnosis increases survival chances. TADA can help them diagnose malignant tumors quicker.
Project Duration and Effort
Type of Prediction
96% accuracy in identifying cancer-causing cell nuclei with TADA versus 79% by clinicians.
No need to be an experienced physician, substantial accuracy available for senior and junior physicians alike.
Speed, once the tool is in place, TADA’s analysis takes a few minutes.
TADA improves early cancer detection by 17%.
Problems to solve
Breast cancer is one of the most common cancers today in women. A breast mass in patients means a tumor. It does not necessarily imply a malignant one. Hence, American oncologists perform a fine needle aspirate (FNA) on the cancer patient.
Then, they examine the resulting cells and extract the cells nuclei features. The most critical step is this feature extraction.
The goal is to select elements of this image that one can measure for further computational analysis. And at the same time, the measures should be representative of cancer. We aim to use elements of the image measured as either a diagnostic or a prognostic indicator. The early detection of cancer is an efficient means to improve the patient’s survival rate.
The typical features extracted are:
concavity (severity of concave portions of the contour)
concave points (number of concave portions of the contour)
and several figures are computed for each feature.
The diagnosis of cancer has been mostly dependent on traditional approaches, using trained professionals’ expertise. However, a senior trained professional is not always available.
TADA’s Machine Learning approach can help automate, in part, the cancer risk prediction. Thus senior and junior professionals alike get access to the same analyzed data from cancer patients. They can provide a better, quicker diagnosis, hence improving survival rates.
Make the distinction between benign and malignant tumors after an FNA rapidly.
Support, improve, and reassure oncologists in their diagnoses.
Improve the accuracy of breast cancer prediction.
It poses the following oncology question:
Can cancer prediction distinguish malignant from benign tumors?