The pancreas plays an important role in glucose metabolism, and developing diabetes or long-term glucose metabolism disturbance may be a prevalent sequela after pancreatectomy. Nevertheless, relative factors of new-onset diabetes after pancreatectomy stay unclear. Radiomics analysis is potential to identify image markers for disease prediction or prognosis. Meanwhile, combination of imaging and electronic medical record (EMR) showed superior performance than imaging or EMR alone in previous studies. One critical step is to identity predictors from high-dimensional features, and it is even more challenging to select and fuse imaging and EMR features. In this work, we develop a radiomics pipeline to assess postoperative new-onset diabetes risk of patients undergoing distal pancreatectomy. Specifically, we extract multiscale image features with 3D wavelet transformation, and include patients' characteristics, body composition and pancreas volume information as clinical features. Then, we propose a multi-view subspace clustering guided feature selection method (MSCUFS) for the selection and fusion of image and clinical features. Finally, a prediction model is constructed with classical machine learning classifier. Experimental results on an established distal pancreatectomy cohort showed that the SVM model with combined imaging and EMR features demonstrated good discrimination, with an AUC value of 0.824, which improved the model with image features alone by 0.037 AUC. Compared with state-of-the-art feature selection methods, the proposed MSCUFS has superior performance in fusing image and clinical features.

Existing Few Shot Segmentation (FS-Seg) methods mostly study a restricted setting where only foreground and background are required to be discriminated and fall short at discriminating multiple classes. In this paper, we focus on a challenging but more practical variant: Generalized Few Shot Segmentation (GFS-Seg), where all SEEN and UNSEEN classes are segmented simultaneously. Previous methods treat the background as a regular class, leading to difficulty in differentiating UNSEEN classes from it at the test stage. To address this issue, we propose Adaptive Background Modeling and Prototype Query Network (ABPNet), in which the background is formulated as the complement of the set of interested classes. With the help of the attention mechanism and a novel meta-training strategy, it learns an effective set difference function that predicts task-specific background adaptively. Furthermore, we design a Prototype Querying (PQ) module that effectively transfers the learned knowledge to UNSEEN classes with a neural dictionary. Experimental results demonstrate that ABPNet significantly outperforms the state-of-the-art method CAPL on PASCAL-5i and COCO-20i, especially on UNSEEN classes. Also, without retraining, ABPNet can generalize well to FS-Seg.