Abstract: A multiple instance learning device for analyzing 3D images, comprises a memory in which a multiple instance learning model is stored and at least one processor electrically connected to the memory, wherein the multiple instance learning model comprises a convolution block configured to derive a feature map for each of 2D instances of a 3D image inputted to the multiple instance learning model, a spatial attention block configured to derive spatial attention maps of the instances from the feature maps derived from the convolution block, an instance attention block configured to receive a result of combining the feature maps and the spatial attention maps and derive an attention score for each instance, and derive an aggregated embedding for the 3D image by aggregating embeddings of the instances according to the attention scores.

Abstract: A multiple instance learning device for analyzing 3D images, comprises a memory in which a multiple instance learning model is stored and at least one processor electrically connected to the memory, wherein the multiple instance learning model comprises a convolution block configured to derive a feature map for each of 2D instances of a 3D image inputted to the multiple instance learning model, a spatial attention block configured to derive spatial attention maps of the instances from the feature maps derived from the convolution block, an instance attention block configured to receive a result of combining the feature maps and the spatial attention maps and derive an attention score for each instance, and derive an aggregated embedding for the 3D image by aggregating embeddings of the instances according to the attention scores.