High-fidelity 3D meshes can be tokenized into one-dimension (1D) sequences and directly modeled using autoregressive approaches for faces and vertices. However, existing methods suffer from insufficient resource utilization, resulting in slow inference and the ability to handle only small-scale sequences, which severely constrains the expressible structural details. We introduce the Latent Autoregressive Network (LANE), which incorporates compact autoregressive dependencies in the generation process, achieving a 6× improvement in maximum generatable sequence length compared to existing methods. To further accelerate inference, we propose the Adaptive Computation Graph Reconfiguration (AdaGraph) strategy, which effectively overcomes the efficiency bottleneck of traditional serial inference through spatiotemporal decoupling in the generation process. Experimental validation demonstrates that LANE achieves superior performance across generation speed, structural detail, and geometric consistency, providing an effective solution for high-quality 3D mesh generation.

Unsupervised visual representation learning has gained significant attention in the computer vision community, driven by recent advancements in contrastive learning. Most existing contrastive learning frameworks rely on instance discrimination as a pretext task, treating each instance as a distinct category. However, this often leads to intra-class collision in a large latent space, compromising the quality of learned representations. To address this issue, we propose a novel contrastive learning method that utilizes randomly generated supervision signals. Our framework incorporates two projection heads: one handles conventional classification tasks, while the other employs a random algorithm to generate fixed-length vectors representing different classes. The second head executes a supervised contrastive learning task based on these vectors, effectively clustering instances of the same class and increasing the separation between different classes. Our method, Contrastive Learning via Randomly Generated Supervision(CLRGS), significantly improves the quality of feature representations across various datasets and achieves state-of-the-art performance in contrastive learning tasks.

Three-dimensional organ and cancer segmentation based on multi-sequence MRI is crucial for assisting clinical diagnosis. However, current automated segmentation methods often focus on specific sequences, specific organs, and specific cancers, i.e., lack of generality. To address this issue, we propose a universal segmentation network for multi-sequence MRI (UniMRISegNet) that can segment multiple organs and cancers. UniMRISegNet features a shared encoder-decoder architecture equipped with contextual prompt generation (CPG) and prompt-conditioned dynamic convolution (PCDC) modules. The CPG module encodes sequence-specific, position-specific, and organ/cancer-specific text prompts as prior information to inform UniMRISegNet about the specific task to be executed. The PCDC module can adaptively generate model weights based on the assigned prompts, enhancing the segmentation capabilities of the UniMRISegNet for specific tasks. To mitigate discrepancies between different sequences of the same organ and capture similarities between related sequences, we design a novel loss function called Semantic-Aware Cosine Similarity Loss (SACSL), which integrates the cosine similarity of text embeddings to reconcile discrepancies and similarities between MRI sequences of the same organ. We created a large-scale annotated multi-sequence, multi-organ, and multi-cancer segmentation workflow (MSOCS), and demonstrated that our UniMRISegNet outperforms other universal networks and single-task networks on MSOCS. Furthermore, the universal weights from MSOCS can be transferred to never-before-seen downstream tasks, achieving superior performance compared to training from scratch.

Although current data augmentation methods are successful to alleviate the data insufficiency, conventional augmentation are primarily intra-domain while advanced generative adversarial networks (GANs) generate images remaining uncertain, particularly in small-scale datasets. In this paper, we propose a parameterized GAN (ParaGAN) that effectively controls the changes of synthetic samples among domains and highlights the attention regions for downstream classification. Specifically, ParaGAN incorporates projection distance parameters in cyclic projection and projects the source images to the decision boundary to obtain the class-difference maps. Our experiments show that ParaGAN can consistently outperform the existing augmentation methods with explainable classification on two small-scale medical datasets.

Pathological examination of nasopharyngeal carcinoma (NPC) is an indispensable factor for diagnosis, guiding clinical treatment and judging prognosis. Traditional and fully supervised NPC diagnosis algorithms require manual delineation of regions of interest on the gigapixel of whole slide images (WSIs), which however is laborious and often biased. In this paper, we propose a weakly supervised framework based on Tokens-to-Token Vision Transformer (WS-T2T-ViT) for accurate NPC classification with only a slide-level label. The label of tile images is inherited from their slide-level label. Specifically, WS-T2T-ViT is composed of the multi-resolution pyramid, T2T-ViT and multi-scale attention module. The multi-resolution pyramid is designed for imitating the coarse-to-fine process of manual pathological analysis to learn features from different magnification levels. The T2T module captures the local and global features to overcome the lack of global information. The multi-scale attention module improves classification performance by weighting the contributions of different granularity levels. Extensive experiments are performed on the 802-patient NPC and CAMELYON16 dataset. WS-T2T-ViT achieves an area under the receiver operating characteristic curve (AUC) of 0.989 for NPC classification on the NPC dataset. The experiment results of CAMELYON16 dataset demonstrate the robustness and generalizability of WS-T2T-ViT in WSI-level classification.

Magnetic resonance imaging (MRI) has played an important role in the rapid growth of medical imaging diagnostic technology, especially in the diagnosis and treatment of brain tumors owing to its non-invasive characteristics and superior soft tissue contrast. However, brain tumors are characterized by high non-uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature. In addition, the labeling of tumor areas is time-consuming and laborious. To address these issues, this study uses a residual grouped convolution module, convolutional block attention module, and bilinear interpolation upsampling method to improve the classical segmentation network U-net. The influence of network normalization, loss function, and network depth on segmentation performance is further considered. In the experiments, the Dice score of the proposed segmentation model reached 97.581%, which is 12.438% higher than that of traditional U-net, demonstrating the effective segmentation of MRI brain tumor images. In conclusion, we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.

Recent studies have shown that the performance of single-image super-resolution methods can be significantly boosted by using deep convolutional neural networks. In this study, we present a novel single-image super-resolution method by introducing dense skip connections in a very deep network. In the proposed network, the feature maps of each layer are propagated into all subsequent layers, providing an effective way to combine the low-level features and high-level features to boost the reconstruction performance. In addition, the dense skip connections in the network enable short paths to be built directly from the output to each layer, alleviating the vanishing-gradient problem of very deep networks. Moreover, deconvolution layers are integrated into the network to learn the upsampling filters and to speedup the reconstruction process. Further, the proposed method substantially reduces the number of parameters, enhancing the computational efficiency. We evaluate the proposed method using images from four benchmark datasets and set a new state of the art.

An automated segmentation method is presented for multi-organ segmentation in abdominal CT images. Dictionary learning and sparse coding techniques are used in the proposed method to generate target specific priors for segmentation. The method simultaneously learns dictionaries which have reconstructive power and classifiers which have discriminative ability from a set of selected atlases. Based on the learnt dictionaries and classifiers, probabilistic atlases are then generated to provide priors for the segmentation of unseen target images. The final segmentation is obtained by applying a post-processing step based on a graph-cuts method. In addition, this paper proposes a voxel-wise local atlas selection strategy to deal with high inter-subject variation in abdominal CT images. The segmentation performance of the proposed method with different atlas selection strategies are also compared. Our proposed method has been evaluated on a database of 150 abdominal CT images and achieves a promising segmentation performance with Dice overlap values of 94.9%, 93.6%, 71.1%, and 92.5% for liver, kidneys, pancreas, and spleen, respectively.

We propose a fully automatic learning-based multi-atlas approach to segment the prostate using multi-channel (T1 and T2) MR images. After affine transformation to the template space, multi-scale features are extracted and separate random forest classifiers are learnt for the prostate region from the most similar T1 and T2 atlases. The probabilities from these two classifiers (T1 and T2) are then fused to obtain a robust probabilistic atlas. Finally, using the probabilistic representation for each voxel, the multi-image graph cuts algorithm is applied on these multi-channel images simultaneously to get the final segmentation. The novelty of the proposed method lies in the use of multi-channel MR images, a decision forest learnt from only the most similar MR images, and the fusion of global and local template-based classifiers for prostate segmentation. We apply this method to a set of 107 prostate images, with 77 randomly selected images used for training and the remaining 30 images for testing. The results are compared to the radiologist's labeled ground truth using cross-validation. The best result is obtained via hybrid approach in which the global classifier trained on T1 images and local template-based classifiers trained on T2 images are fused to obtain the final probability for each voxel. Our results indicate that the proposed method is robust, capable of producing accurate segmentation automatically and most importantly, not patient-specific.

In this paper, a novel grey rough set model for interval-valued grey sets information systems named interval-valued grey-rough set model is proposed and the basic theory and propositions of the interval-valued grey-rough sets is studied. Based on the proposed interval-valued rough grey model, rough similarity degree is defined, and the clustering of the interval-valued grey information systems is also examined, in the meanwhile, some examples are presented respectively.