PEMMA: Parameter-Efficient Multi-Modal Adaptation for Medical Image Segmentation

arXiv:2404.13704v1 Announce Type: cross
Abstract: Imaging modalities such as Computed Tomography (CT) and Positron Emission Tomography (PET) are key in cancer detection, inspiring Deep Neural Networks (DNN) models that merge these scans for tumor segmentation. When both CT and PET scans are available, it is common to combine them as two channels of the input to the segmentation model. However, this method requires both scan types during training and inference, posing a challenge due to the limited availability of PET …

abstract arxiv cancer cancer detection channels cs.cv cs.lg detection dnn eess.iv image imaging key medical merge modal multi-modal networks neural networks pet scans segmentation them type