Recently, deep learning has started to play an essential role in healthcare
applications, including image search in digital pathology. Despite the recent
progress in computer vision, significant issues remain for image searching in
histopathology archives. A well-known problem is AI bias and lack of
generalization. A more particular shortcoming of deep models is the ignorance
toward search functionality. The former affects every model, the latter only
search and matching. Due to the lack of ranking-based learning, researchers
must train models based on the classification error and then use the resultant
embedding for image search purposes. Moreover, deep models appear to be prone
to internal bias even if using a large image repository of various hospitals.
This paper proposes two novel ideas to improve image search performance. First,
we use a ranking loss function to guide feature extraction toward the
matching-oriented nature of the search. By forcing the model to learn the
ranking of matched outputs, the representation learning is customized toward
image search instead of learning a class label. Second, we introduce the
concept of sequestering learning to enhance the generalization of feature
extraction. By excluding the images of the input hospital from the matched
outputs, i.e., sequestering the input domain, the institutional bias is
reduced. The proposed ideas are implemented and validated through the largest
public dataset of whole slide images. The experiments demonstrate superior
results compare to the-state-of-art.Comment: Under Review for publicatio