Deep learning enhances cell image analysis capabilities

Cells are the basic structure and function of life, varying
in size, shape, and density.
There are many different physiological and pathological factors that affect these parameters
.
Therefore, studying the properties of cells is extremely important
for biomedical and pharmaceutical research.
Traditionally, researchers have looked directly at cell samples through a microscope to study morphological changes
in cells.
In recent years, with the development of computer science and artificial intelligence, deep learning can now be combined
with cell analysis methods.
This can replace direct observation and manual interpretation of images under the microscope, improving the efficiency and accuracy
of research.

In recent years, more and more deep learning-based algorithms have been developed to support cell image analysis, mainly to solve three key tasks:

1. Segmentation
   .
   To identify meaningful objects or features, deep learning is used to divide the image into parts
   .
   Cell segmentation is the basic prerequisite for identifying, counting, tracking and morphological analysis of cell images;

2. Tracking
   .
   That is, after segmenting the cell image, the cell behavior is monitored
   over the entire spectrum.
   Living cells contain a lot of information about an organism, and the dynamic properties of cells, especially morphological changes, can reflect the health status of the organism in pathophysiological processes, such as immune response, wound healing, cancer cell spread and metastasis
   .
   

3. Classification
   .
   Classification of cell morphological features based on extraction parameters is often used as a downstream analytical task
   for phenotypic screening and cell analysis.
   

In response to the above three key tasks, a review article published in the journal Intelligent Computing discusses the progress of
deep learning technology in the above fields in depth.
"Compared to traditional computer vision techniques, deep neural networks (DNNs) can automatically generate representations that are more efficient than handmade by learning from large-scale data sets
.
In cell images, deep learning-based methods have also shown promising results
in cell segmentation and tracking.
"This successful application demonstrates DNN's ability to extract high-level features and reveals the potential ability
to use deep learning to reveal more complex life patterns behind cellular phenotypes," the authors said.

In addition, the authors discuss the challenges and opportunities
of deep learning methods in cell image processing.
"Deep learning has demonstrated its amazing ability
to perform cell image analysis," the authors said.
However, there is still a big performance gap
between deep learning algorithms in academic research and practical applications.
"There are challenges and opportunities in three areas: data quantity, data quality and data confidence:

1. Deep learning
   based on small and expensive datasets.
   Building large-scale cell image datasets is a daunting task
   .
   This is because cell images require knowledgeable biological experts to assign labels
   image by image.
   The size of cell image datasets is often limited
   by the difficulty of labeling.
   

2. Deep learning with noise and unbalanced labels.
   The annotation quality of cell image datasets is highly dependent on human expertise, resulting in label noise and label imbalance.
   Label noise is generated
   by assigning incorrect or incomplete labels to training images.
   Tag imbalance is due to a preference for annotations, where the number of tagged images of different classes is quite
   unbalanced.
   

3. Image analysis
   of uncertainty-aware cells.
   Uncertainty-aware learning is essential
   for deep learning applications in biological scenarios.
   Without a mechanism that reflects the confidence level of classification results, it is impossible for ordinary neural networks to detect new phenotypes
   .
   

Through deep learning, scientists are exploring new techniques to improve cell image analysis
.
More effective solutions will be proposed in the future, and deep learning and biomedical research will be more closely integrated
.