Existing Medical Visual Question Answering (Med-VQA) methods typically rely on either direct answer generation or Chain-of-Thought (CoT) reasoning, both of which suffer from limited interpretability or logical inconsistency. To overcome these challenges, we propose Med-SER, a novel framework featuring Structured Chain-of-Thought (SCoT), which decomposes reasoning into four clinically grounded stages: Summary, Caption, Reasoning, and Conclusion. To facilitate training, we construct VQA-RAD-SCoT, the first Med-VQA dataset annotated with structured reasoning chains. Med-SER further introduces a Dual-Channel Visual Projection (DCVP) module to extract both holistic and stage-specific visual features, and a Dual Dynamic Supervision (DDS) mechanism combining adaptive stage-aware weighting and logical consistency loss. Experiments on VQA-RAD-SCoT demonstrate that Med-SER demonstrates the potential of Med-SER to establish a new interpretable and trustworthy paradigm for Med-VQA.

Sparse view CBCT reconstruction has become one of the important research fields to reduce the radiation impact of CT scanning. However, the reconstruction of high-quality 3D CT volumes from sparse and noisy CBCT data still faces challenges such as slow convergence, long computation time, and increased noise. In light of these issues, we propose a voxel attenuation grid representation to explicitly model the attenuation field of the 3D CT volume. Since this representation does not involve the implementation of neural networks, our method for reconstruction is extremely fast. Furthermore, trim regularization and total variation regularization terms are introduced on top of the mean square error loss to optimize the voxel attenuation grid and significantly reduce the noise in the reconstructed 3D CT volume. Experiments on the NSCLC dataset demonstrate the superiority of our method and its potential in clinical applications. Our code will be available at: https://github.com/qiaodongxing/VAG.

Deep neural networks boost many successful colorization methods, including automatic, interactive, and exemplar-based methods. Among them, interactive methods with global and/or local inputs are probably the most flexible to accurately add colors to a gray image. However, due to the sparseness of input-semantic correspondences, existing methods encounter difficulties in distributing inputs into correct regions. Moreover, they simply add or concatenate the features of different inputs to the network before color reconstruction, which cannot balance the influences of different inputs. To this end, we propose a novel interactive colorization network, which explicitly builds input-semantic correspondences with an attention mechanism and proposes a gated feature fusion module to balance the influences of global and local inputs. We further apply a differentiable histogram loss to impose a smooth impact of the global inputs. Extensive experiments demonstrate that our method can flexibly control the results and outperforms other state-of-the-art interactive methods.

Recent methods based on deep learning have shown promise in converting grayscale images to colored ones. However, most of them only allow limited user inputs (no inputs, only global inputs, or only local inputs), to control the output colorful images. The possible difficulty lies in how to differentiate the influences of different inputs. To solve this problem, we propose a two-stage deep colorization method allowing users to control the results by flexibly setting global inputs and local inputs. The key steps include enabling color themes as global inputs by extracting K mean colors and generating K-color maps to define a global theme loss, and designing a loss function to differentiate the influences of different inputs without causing artifacts. We also propose a color theme recommendation method to help users choose color themes. Based on the colorization model, we further propose an image compression scheme, which supports variable compression ratios in a single network. Experiments on colorization show that our method can flexibly control the colorized results with only a few inputs and generate state-of-the-art results. Experiments on compression show that our method achieves much higher image quality at the same compression ratio when compared to the state-of-the-art methods.

Sketch recognition using deep neural networks have become a recent trend. However, traditional pixel (image) based convolutional neural networks show poor recognizing performance on low resolution (LR) sketch image due to the loss of image details. To solve this problem, we propose a joint pixel and point convolutional neural network for LR sketch image recognition. The network, equipped with both image convolution and point convolution, can simultaneously handle both the image and point representation of sketches. Furthermore, we propose a hybrid classifier, a corresponding loss function, and a training scheme to better extract features for recognition. Experimental results show that our method outperforms state-of-art deep neural networks.

Colorization methods using deep neural networks have become a recent trend. However, most of them do not allow user inputs, or only allow limited user inputs (only global inputs or only local inputs), to control the output colorful images. The possible reason is that it's difficult to differentiate the influence of different kind of user inputs in network training. To solve this problem, we propose a novel deep colorization method allowing inputting global and local inputs simultaneously or individually, which is not supported in previous deep colorization methods. The key steps include designing a neural network model that can appropriately combine the different inputs, and designing an appropriate loss function that can differentiate the influence of different inputs. Experimental results show that our method can magnificently control the colorized results and generate state-of-art results.