Authors - Shweta B. Barshe, Garima B. Shukla Abstract - The use of artificial intelligence (AI), especially deep learning, has brought major changes in the study of histopathology images for cancer diagnosis for the doctors. This review compares the latest deep learning methods used to identify whether the tissues are Benign. (noncancerous) or malignant (cancerous). This paper discusses different technologies used for the study of histopathology images. Convolutional neural network (CNNs) is effective in capturing small local details in the images. Several studies report that Transformers (ViTs) can outperform CNNs in complex classification tasks [18, 22]. Along with the discussion about newer hybrid models and large foundation models, the paper specifies the strength of combining the strength of both. Although these models are developed focusing on achieving high accuracy on good data sets, there are few challenges in their practical use, such as i. Models often fail to generalize data from different hospitals due to domain shift [1, 28] ii. Model interpretability remains a significant challenge in clinical development [34]. iii. There is a lack of proper methods to measure the uncertainty in the decisions [40,41]. This paper highlights the research gaps in real clinical use and focuses on the need to develop models that are robust, interpretable, and suitable for practical healthcare applications.
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