Fig. 1: Overview of CAMP for patch-level classification.
From: CAMP: continuous and adaptive learning model in pathology
The numbers in the brackets index the order of the procedure. a For each patch classification task, the image-text prompt input and text ground truth are generated. The patch query generation is generated by a pre-trained visual encoder and a pre-trained text decoder. b During training, \({{\mathcal{L}}}_{{\mathcal{S}}}\) is used for optimizing adapters, whereas \({{\mathcal{L}}}_{{\mathcal{K}}}\) is utilized for updating a key. This process only updates the training task and preserves the knowledge of previously learned tasks. c During inference, a query is generated based on an input to retrieve the most suitable adapters. After being integrated with the adapters, CAMP generates a textual prediction.
