Machine learning radically reduces workload of cell counting for disease diagnosis

Beijing Institute of Technology Press Co., Ltd

The use of machine learning to perform blood cell counts for diagnosis of disease instead of expensive and often less accurate cell analyzer machines has nevertheless been very labor-intensive as it takes an enormous amount of manual annotation work by humans in the training of the machine learning model. However, researchers at Benihang university have developed a new training method that automates much of this activity.

Their new training scheme is described in a paper published in the journal of Cyborg and Bionic Systems on April 9.

The number and type of cells in the blood often play a crucial role in disease diagnosis, but the cell analysis techniques commonly used to perform such counting of blood cells—involving the detection and measurement of physical and chemical characteristics of cells suspended in fluid—are expensive and require complex preparations. Worse still, the accuracy of cell analyzer machines is only about 90 percent due to various influences such as temperature, pH, voltage, and magnetic field that can confuse the equipment.

In order to improve accuracy, reduce complexity and lower costs, much research into alternatives has lately focussed on the use of computer programs to perform “segmentation” on photographs of the blood taken by a high-definition camera connected to a microscope. Segmentation involves algorithms that perform pixel-by-pixel labeling of what appears in a photo, in this case, what parts of the image are cells and which are not—in essence, counting the number of cells in an image.

/Public Release. This material from the originating organization/author(s) may be of a point-in-time nature, edited for clarity, style and length. The views and opinions expressed are those of the author(s).View in full here.