In contemporary radiotherapy planning (RTP), a key module leaf sequencing is predominantly addressed by optimization-based approaches. In this paper, we propose a novel deep reinforcement learning (DRL) model termed as Reinforced Leaf Sequencer (RLS) in a multi-agent framework for leaf sequencing. The RLS model offers improvements to time-consuming iterative optimization steps via large-scale training and can control movement patterns through the design of reward mechanisms. We have conducted experiments on four datasets with four metrics and compared our model with a leading optimization sequencer. Our findings reveal that the proposed RLS model can achieve reduced fluence reconstruction errors, and potential faster convergence when integrated in an optimization planner. Additionally, RLS has shown promising results in a full artificial intelligence RTP pipeline. We hope this pioneer multi-agent RL leaf sequencer can foster future research on machine learning for RTP.

Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. However, medical image datasets are often low in sample size and only partially labeled, i.e., only a subset of organs are annotated. Therefore, it is crucial to investigate how to learn a unified model on the available partially labeled datasets to leverage their synergistic potential. In this paper, we systematically investigate the partial-label segmentation problem with theoretical and empirical analyses on the prior techniques. We revisit the problem from a perspective of partial label supervision signals and identify two signals derived from ground truth and one from pseudo labels. We propose a novel two-stage framework termed COSST, which effectively and efficiently integrates comprehensive supervision signals with self-training. Concretely, we first train an initial unified model using two ground truth-based signals and then iteratively incorporate the pseudo label signal to the initial model using self-training. To mitigate performance degradation caused by unreliable pseudo labels, we assess the reliability of pseudo labels via outlier detection in latent space and exclude the most unreliable pseudo labels from each self-training iteration. Extensive experiments are conducted on one public and three private partial-label segmentation tasks over 12 CT datasets. Experimental results show that our proposed COSST achieves significant improvement over the baseline method, i.e., individual networks trained on each partially labeled dataset. Compared to the state-of-the-art partial-label segmentation methods, COSST demonstrates consistent superior performance on various segmentation tasks and with different training data sizes.

Deep learning has been utilized in knowledge-based radiotherapy planning in which a system trained with a set of clinically approved plans is employed to infer a three-dimensional dose map for a given new patient. However, previous deep methods are primarily limited to simple scenarios, e.g., a fixed planning type or a consistent beam angle configuration. This in fact limits the usability of such approaches and makes them not generalizable over a larger set of clinical scenarios. Herein, we propose a novel conditional generative model, Flexible-Cm GAN, utilizing additional information regarding planning types and various beam geometries. A miss-consistency loss is proposed to deal with the challenge of having a limited set of conditions on the input data, e.g., incomplete training samples. To address the challenges of including clinical preferences, we derive a differentiable shift-dose-volume loss to incorporate the well-known dose-volume histogram constraints. During inference, users can flexibly choose a specific planning type and a set of beam angles to meet the clinical requirements. We conduct experiments on an illustrative face dataset to show the motivation of Flexible-Cm GAN and further validate our model's potential clinical values with two radiotherapy datasets. The results demonstrate the superior performance of the proposed method in a practical heterogeneous radiotherapy planning application compared to existing deep learning-based approaches.

Body part regression is a promising new technique that enables content navigation through self-supervised learning. Using this technique, the global quantitative spatial location for each axial view slice is obtained from computed tomography (CT). However, it is challenging to define a unified global coordinate system for body CT scans due to the large variabilities in image resolution, contrasts, sequences, and patient anatomy. Therefore, the widely used supervised learning approach cannot be easily deployed. To address these concerns, we propose an annotation-free method named blind-unsupervised-supervision network (BUSN). The contributions of the work are in four folds: (1) 1030 multi-center CT scans are used in developing BUSN without any manual annotation. (2) the proposed BUSN corrects the predictions from unsupervised learning and uses the corrected results as the new supervision; (3) to improve the consistency of predictions, we propose a novel neighbor message passing (NMP) scheme that is integrated with BUSN as a statistical learning based correction; and (4) we introduce a new pre-processing pipeline with inclusion of the BUSN, which is validated on 3D multi-organ segmentation. The proposed method is trained on 1,030 whole body CT scans (230,650 slices) from five datasets, as well as an independent external validation cohort with 100 scans. From the body part regression results, the proposed BUSN achieved significantly higher median R-squared score (=0.9089) than the state-of-the-art unsupervised method (=0.7153). When introducing BUSN as a preprocessing stage in volumetric segmentation, the proposed pre-processing pipeline using BUSN approach increases the total mean Dice score of the 3D abdominal multi-organ segmentation from 0.7991 to 0.8145.

The key point of face recognition is creating a discriminative feature representation to ensure intraclass compactness and interclass separability. Softmax loss is widely used in deep learning networks, but it is indirect for face verification. Center loss is effective to improve intraclass compactness, while interclass distances are ignored. In this letter, we propose a novel loss function, termed margin loss, to enlarge distances of interclass and reduce intraclass variations simultaneously. Margin loss aims to focus on samples hard to classify by a distance margin. Different from Softmax loss, margin loss is based on Euclidean distances that can directly measure face similarity. Experiments on different datasets have demonstrated the effectiveness of our method.

Face images captured by surveillance videos usually have limited resolution. Due to resolution mismatch, it is hard to match high-resolution (HR) faces with low-resolution (LR) faces directly. Recently, multidimensional scaling (MDS) has been employed to solve the problem. In this letter, we proposed a more discriminative MDS method to learn a mapping matrix, which projects the HR images and LR images to a common subspace. Our method is discriminative since both interclass distances and intraclass distances are taken into consideration. We add an interclass constraint to enlarge the distances of different subjects in the subspace to ensure discriminability. Besides, we consider not only the relationship of HR-LR images, but also the relationship of HR-HR images and LR-LR images in order to preserve local consistency. Experimental results on FERET, Multi-PIE, and SCface databases demonstrate the effectiveness of our proposed approach.

In this paper, we propose Two-stage Patch-based Sparse Multi-value Descriptor (TPSMD), a generalization of Sparse Linear Regression Binary method. The TPSMD makes two contributions. First, the multi-value strategy introduces user-specified parameters to improve the binarization, which makes our method more discriminant and less sensitive to noise. The multi-value strategy is a comprise between the simplification and discrimination. Second, the two-stage patch-based strategy contains two independent patch-segmentations for the face image. In the first stage, according to the Multi-value strategy we obtain the discriminative local descriptor based on small patches. In the second stage, we calculate weights for larger patches, and the discriminative face regions, such as eyes and month, are strengthened by the weights. The Two-stage strategy considers local similarity in the first stage and global differences in the second one. Extensive experiments on Extended Yale B and FERET show that our method outperforms state-of-the-art methods.