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Histopathological tissue classification is a fundamental task in pathomicscancer research. Precisely differentiating different tissue types is a benefitfor the downstream researches, like cancer diagnosis, prognosis and etc.Existing works mostly leverage the popular classification backbones in computervision to achieve histopathological tissue classification. In this paper, weproposed a super lightweight plug-and-play module, named Pyramidal Deep-BroadLearning (PDBL), for any well-trained classification backbone to furtherimprove the classification performance without a re-training burden. We mimichow pathologists observe pathology slides in different magnifications andconstruct an image pyramid for the input image in order to obtain the pyramidalcontextual information. For each level in the pyramid, we extract themulti-scale deep-broad features by our proposed Deep-Broad block (DB-block). Weequipped PDBL in three popular classification backbones, ShuffLeNetV2,EfficientNetb0, and ResNet50 to evaluate the effectiveness and efficiency ofour proposed module on two datasets (Kather Multiclass Dataset and the LC25000Dataset). Experimental results demonstrate the proposed PDBL can steadilyimprove the tissue-level classification performance for any CNN backbones,especially for the lightweight models when given a small among of trainingsamples (less than 10%), which greatly saves the computational time andannotation efforts.

PDBL: Improving Histopathological Tissue Classification with Plug-and-Play Pyramidal Deep-Broad Learning