en AI in Medicine پژوهشي Original Article <div style="text-align: justify;"><span style="line-height:2;"><span style="font-size:14px;"><span style="font-family:Times New Roman;"><strong>Objective:&nbsp;</strong>To propose a deep learning-based hierarchical framework for content-based histopathology image retrieval (CBHIR) that can serve as a diagnostic aid for pathologists.<br> <strong>Methodology: </strong>The framework extracts deep features using a pre-trained EfficientNet-B0 model, followed by global average pooling (GAP) for dimensionality reduction. A triplet network with a custom loss function is designed for deep hashing; the loss function leverages cosine similarity and incorporates data engineering by selecting the most challenging samples in the feature space to enhance retrieval performance. The model is evaluated on the Kimia path24c dataset and compared with baseline methods.<br> <strong>Results:&nbsp;</strong>The proposed framework achieved an accuracy of 98.76% for patch-level retrieval, 98.61% for whole-slide retrieval, and a total accuracy of 97.39% in publicly available Kimia path24c dataset.<br> <strong>Conclusion:</strong> The experimental results demonstrate the efficiency of the proposed framework for the CBHIR task. Moreover, the output of this work can be used in diagnostic practice to help pathologists analyze digital histopathology samples, as well as in education to support training of pathology professionals.</span></span></span></div> Artificial intelligence (AI), Deep learning , Content-based image retrieval (CBIR), Digital pathology , Triplet network , Diagnosis, Cosine similarity 15 24 http://ijbc.ir/browse.php?a_code=A-10-2398-1&slc_lang=en&sid=1 Amin Heyrani Khameneh 100319475328460013337 100319475328460013337 No Department of Electrical and Computer Engineering, Urmia University, Urmia, Iran. Morteza Valizadeh mo.valizadeh@urmia.ac.ir 100319475328460013338 100319475328460013338 Yes Department of Electrical and Computer Engineering, Urmia University, Urmia, Iran. Mehdi Chehel Amirani 100319475328460013339 100319475328460013339 No Department of Electrical and Computer Engineering, Urmia University, Urmia, Iran. Seyed Sadra Kashef 100319475328460013340 100319475328460013340 No Department of Electrical and Computer Engineering, Urmia University, Urmia, Iran.