Jinquan Sun

Jinquan Sun, Yinghuan Shi, Yang Gao et al.

Due to low tissue contrast, irregular object appearance, and unpredictable location variation, segmenting the objects from different medical imaging modalities (e.g., CT, MR) is considered as an important yet challenging task. In this paper, we present a novel method for interactive medical image segmentation with the following merits. (1) Our design is fundamentally different from previous pure patch-based and image-based segmentation methods. We observe that during delineation, the physician repeatedly check the inside-outside intensity changing to determine the boundary, which indicates that comparison in an inside-outside manner is extremely important. Thus, we innovatively model our segmentation task as learning the representation of the bi-directional sequential patches, starting from (or ending in) the given central point of the object. This can be realized by our proposed ConvRNN network embedded with a gated memory propagation unit. (2) Unlike previous interactive methods (requiring bounding box or seed points), we only ask the physician to merely click on the rough central point of the object before segmentation, which could simultaneously enhance the performance and reduce the segmentation time. (3) We utilize our method in a multi-level framework for better performance. We systematically evaluate our method in three different segmentation tasks including CT kidney tumor, MR prostate, and PROMISE12 challenge, showing promising results compared with state-of-the-art methods. The code is available here: \href{https://github.com/sunalbert/Sequential-patch-based-segmentation}{Sequential-patch-based-segmentation}.

Ling Team, Ang Li, Ben Liu et al.

We introduce Ling 2.0, a series reasoning-oriented language foundation built upon the principle that every activation boosts reasoning capability. Designed to scale from tens of billions to one trillion parameters under a unified Mixture-of-Experts (MoE) paradigm, Ling 2.0 emphasizes high sparsity, cross-scale consistency, and efficiency guided by empirical scaling laws. The series includes three non-thinking (instruct) models - Ling-mini-2.0, Ling-flash-2.0, and Ling-1T - ranging from 16B to 1T total parameters and achieving up to 7-fold active-compute efficiency compared with dense counterparts. Ling 2.0 integrates coordinated innovations across model architecture, pre-training, post-training, and infrastructure: a high-sparsity MoE with MTP for efficient reasoning, reasoning-oriented data and mid-training CoT activation, reinforcement-based fine-tuning (DFT, Evo-CoT), and full-scale FP8 training with fine-grained heterogeneous pipelines. At the trillion scale, Ling-1T establishes a new Pareto frontier of reasoning accuracy versus computational efficiency, demonstrating that sparse activation, when properly aligned with reasoning objectives, enables scalable and efficient intelligence. Collectively, Ling 2.0 provides a coherent, open, and efficient foundation for advancing future reasoning and thinking models, including the Ring series built upon the same base.

Qian Yu, Yinghuan Shi, Jinquan Sun et al.

Due to the irregular motion, similar appearance and diverse shape, accurate segmentation of kidney tumor in CT images is a difficult and challenging task. To this end, we present a novel automatic segmentation method, termed as Crossbar-Net, with the goal of accurate segmenting the kidney tumors. Firstly, considering that the traditional learning-based segmentation methods normally employ either whole images or squared patches as the training samples, we innovatively sample the orthogonal non-squared patches (namely crossbar patches), to fully cover the whole kidney tumors in either horizontal or vertical directions. These sampled crossbar patches could not only represent the detailed local information of kidney tumor as the traditional patches, but also describe the global appearance from either horizontal or vertical direction using contextual information. Secondly, with the obtained crossbar patches, we trained a convolutional neural network with two sub-models (i.e., horizontal sub-model and vertical sub-model) in a cascaded manner, to integrate the segmentation results from two directions (i.e., horizontal and vertical). This cascaded training strategy could effectively guarantee the consistency between sub-models, by feeding each other with the most difficult samples, for a better segmentation. In the experiment, we evaluate our method on a real CT kidney tumor dataset, collected from 94 different patients including 3,500 images. Compared with the state-of-the-art segmentation methods, the results demonstrate the superior results of our method on dice ratio score, true positive fraction, centroid distance and Hausdorff distance. Moreover, we have extended our crossbar-net to a different task: cardiac segmentation, showing the promising results for the better generalization.