Illustration of features learning and results of multi-magnification pathological image computing method proposed by the research team. Credit: Qin Wenjian
Typically for clinical applications, pathologists will usually fuse histopathological images at different magnifications, from the subnuclear (0.1 μm) all the way up to larger tissues (1mm) for diagnosis.
These magnification-based learning networks that combine information at different magnifications, have attracted attention for their ability to improve performance in histopathological classification with the precise classification of histopathological images being crucial to computer-aided diagnosis in clinical practice. However, the ability to fuse histopathological images at different magnifications remains a very much deprived area within current research.
In this paper, a research team led by Dr. Qin Wenjian from the Shenzhen Institute of Advanced Technology (SIAT) of the Chinese Academy of Sciences, together with Prof. Luo Weiren’s team from Shenzhen Third People’s Hospital and Prof. Nazar Mustafa Zaki’s team from United Arab Emirates University, proposed a novel deep multi-magnification similarity learning (DSML) approach that can be useful for the interpretation of multi-magnification learning framework and easy to visualize feature representation from low-dimension (e.g., cell-level) to high-dimension (e.g., tissue-level), which has overcome the difficulty of understanding cross-magnification information propagation.
This approach uses a similarity cross entropy loss function designation to simultaneously learn the similarity of the information among cross-magnifications. In order to verify the effectiveness of DMSL, experiments with different network backbones and different magnification combinations were designed, and its ability to interpret was also investigated through visualization.
The study was published in IEEE Journal of Biomedical and Health Informatics on Jan. 16.
In order to verify the effectiveness of DMSL, experiments with different network backbones and different magnification combinations were designed, and its ability to interpret was also investigated through visualization.
“Our experiments were performed on two different histopathological datasets: a clinical nasopharyngeal carcinoma and a public breast cancer BCSS2021 dataset. The results show that our method achieved outstanding performance in classification with a higher value of area under curve, accuracy, and F-score than other comparable methods. Moreover, the reasons behind multi-magnification effectiveness were discussed.” said Dr. Qin.
The researchers designed different network backbones and magnification combinations to verify the effectiveness of DMSL on a clinical nasopharyngeal carcinoma and a public breast cancer BCSS2021 dataset.
They also investigated its ability to interpret. The results showed that it performed better in classification with a higher value of area under curve, accuracy and F-score than other comparable methods.
More Information & Source:
Songhui Diao et al, Deep Multi-Magnification Similarity Learning for Histopathological Image Classification, IEEE Journal of Biomedical and Health Informatics (2023). DOI: 10.1109/JBHI.2023.3237137
For further information: Chinese Academy of Sciences