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A recent study conducted in Valencia, Spain, highlights the potential of an unsupervised Convolutional Auto Encoder (CAE) approach in improving the accuracy and efficiency of cancer diagnosis. The study focuses on Content-Based Medical Image Retrieval (CBMIR), which allows pathologists to search for similar histopathological Whole Slide Images (WSIs) to enhance diagnostic reliability. The customized CAE, known as Unsupervised CBMIR (UCBMIR), was evaluated using various techniques and achieved high recall and precision rates. The tool also demonstrated the ability to identify diverse patterns and retrieve images with the same cancer type. This research offers a promising step towards more transparent and accurate cancer diagnosis. [Extracted from the article]