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Found 5 papers, 2 papers with code
Enhancing Content-based Recommendation via Large Language Model
However, they still neglect the following two points: (1) The content semantic is a universal world knowledge; how do we extract the multi-aspect semantic information to empower different domains?
UniMOS: A Universal Framework For Multi-Organ Segmentation Over Label-Constrained Datasets
However, due to the fact that medical image annotation requires a great deal of manpower and expertise, as well as the fact that clinical departments perform image annotation based on task orientation, there is the problem of having fewer medical image annotation data with more unlabeled data and having many datasets that annotate only a single organ.
Data and Knowledge Co-driving for Cancer Subtype Classification on Multi-Scale Histopathological Slides
In this paper, we propose a Data and Knowledge Co-driving (D&K) model to replicate the process of cancer subtype classification on a histopathological slide like a pathologist.
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation
Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis.
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Images for Segmentation
To mitigate this shortcoming, we propose a novel group equivariant segmentation framework by encoding those inherent symmetries for learning more precise representations.
