Multi-task learning (MTL) has become a research hotspot for the analysis of whole-slide histopathological images (WSIs) since it can capture the shared representations of different tasks for the improvement of individual tasks. However, the shared representations learned by MTL are always dominated by the tasks appearing in the training set that is difficult to directly apply it on the unseen (new) tasks, especially when the unseen tasks are significantly different from the known tasks. To address the above issues, we develop a Task-Agnostic Feature-Learner (TAFL) for efficient adaptation to unseen clinical tasks, which can leverage useful image information from the existing tasks for new clinical trials with minimal task-specific modifications. Specifically, we firstly develop a neural architecture search (NAS) module that can design the network architectures of TAFL automatically. Then, a novel task-level meta-learning algorithm is developed to extract efficient and universal information from the known tasks for improving the prediction performance on the unseen tasks. We evaluate our method on three publicly available datasets derived from The Cancer Genome Atlas (TCGA) for various clinical prediction tasks (i.e., staging, cancer subtyping and survival prediction), and the experimental results indicate that our TAFL can effectively adapt to unseen tasks with better prediction performance.

Sparse-View CT (SVCT), which provides low-dose and high-speed CT imaging, plays an important role in the medical imaging area. As the decrease of projection views, the reconstructed image suffers from severe artifacts. To this end, recent works utilize deep learning methods to improve the imaging quality of SVCT and achieve promising performances. However, these methods mainly focus on the network design and modeling but overlook the importance of choosing projection views. To address this issue, this paper proposes a Projection-view LeArning Network (PLANet), which can estimate the importance of different view angles through reconstruction network training and select the projection views for high-quality image restoration. Specifically, we generate synthesized sparse-view sinograms by subsampling projections from full-view sinograms based on a learnable distribution, which can be learned through reconstruction network training. Thus, important image views can be selected to acquire sparse-view projection in imaging equipment. Furthermore, effective data augmentations are provided by the online generation of sparse-view sinogram to improve the stability and performance of reconstruction networks. In short, our method can select the important projection views and learn high-performance reconstruction networks in one unified deep-learning framework. Comprehensive experiments show that the proposed method achieves promising results compared to state-of-the-art methods, and the ablation studies also show the superiority of our proposed PLANet in terms of effectiveness and robustness.