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In the dynamic landscape of medical artificial intelligence, this study explores the vulnerabilities of the Pathology Language-Image Pretraining (PLIP) model, a Vision Language Foundation model, under targeted attacks like PGD adversarial attack.
An automated CAD (Computer aided diagnosis) system for the classification of the breast histopathology images. It contains machine learning (feature extraction) and deep learning (transfer learning) pipelines.
Following repository demonstrates machine learning architectures that can correctly classify lesions between LM and AMH. Overall, our methods showcase the potential for computer-aided diagnosis in dermatology, which, in conjunction with remote acquisition can expand the range of diagnostic tools in the community. This code is implemented using K…
This file contains the code for my work. "Reference5" is the image used for normalization. The "stained_dataset_test" is the final result of the model. The "lc25000_dataset_test" is the result of my model on the Augmentor dataset. Lastly, the "vahadane_stain_normalization" is my normalization code.