Pancreatic cancer is characterized by insidious onset and extremely poor prognosis. Accurate segmentation of the pancreas and its tumors is crucial for achieving precision medicine. The pancreas presents as a small, low-contrast target, while pancreatic tumors appear even smaller with highly irregular shapes. Existing methods, such as Convolutional Neural Networks, are limited by their small receptive fields and thus struggle to model long-range dependencies. Meanwhile, the fixed computational path of Transformers presents challenges for their adaptation to 3D pancreas and tumor segmentation. This study proposes the Global Awareness and Local Refinement (GALR) model, which addresses the challenges of long-range dependencies and multi-scale modeling, achieving precise 3D segmentation of the pancreas and its tumor boundaries. The model consists of two core innovative modules: The Multi-scale Dual Attention Mamba (MDAM) module dynamically captures the key information of the remodeling sequence through the Selective State Space Model, and combines the dilated convolution multi-scale fusion to enhance the multi-scale object recognition ability. The module integrates the dual attention mechanism to capture the cross-regional anatomical correlation, strengthens the segmentation sensitivity of low contrast regions, and realizes global perception of precision medical image segmentation. The Lightweight Grouped Convolutional Enhancement (LGCE) module uses a parameter-efficient linkage structure to construct a feature refinement flow for small target boundaries in the decoding stage, which effectively suppresses the interference of noise such as artifacts on the target contour. This module significantly improves the localization accuracy through the Local Refinement mechanism, and achieves accurate boundary contour segmentation. Experimental results on two datasets, CT and MRI, show that GALR has the best performance in pancreas and tumor segmentation tasks. On dataset 1, our method achieves Dice coefficients of 80.27 % and 51.72 % in pancreas and tumor segmentation tasks, respectively, which are 0.46 % and 1.18 % higher than the previous best model. The performance of other indicators is also excellent. The experiment on dataset 2 also leads to the same conclusion. The experimental results indicate that GALR provides an efficient clinical solution for computeraided diagnosis of pancreatic diseases.

Achieving accurate bladder wall and tumor segmentation from MRI is critical for diagnosing and treating bladder cancer. However, automated segmentation remains challenging due to factors such as comparable density distributions, intricate tumor morphologies, and unclear boundaries. Considering the attributes of bladder MRI images, we propose an efficient multiscale hierarchical hybrid attention with a transformer (MH2AFormer) for bladder cancer and wall segmentation. Specifically, a multiscale hybrid attention and transformer (MHAT) module in the encoder is designed to adaptively extract and aggregate multiscale hybrid feature representations from the input image. In the decoder stage, we devise a multiscale hybrid attention (MHA) module to generate high-quality segmentation results from multiscale hybrid features. Combining these modules enhances the feature representation and guides the model to focus on tumor and wall regions, which helps to solve bladder image segmentation challenges. Moreover, MHAT utilizes the Fast Fourier Transformer with a large kernel (e.g., 224 × 224) to model global feature relationships while reducing computational complexity in the encoding stage. The model performance was evaluated on two datasets. As a result, the model achieves relatively best results regarding the intersection over union (IoU) and dice similarity coefficient (DSC) on both datasets (Dataset A: IoU = 80.26%, DSC = 88.20%; Dataset B: IoU = 89.74%, DSC = 94.48%). These advantageous outcomes substantiate the practical utility of our approach, highlighting its potential to alleviate the workload of radiologists when applied in clinical settings.

Jigsaw puzzles consist of reconstructing a picture that has been divided into many interlocking pieces. This paper describes an automatic global method for solving the square-piece jigsaw puzzle problem in which neither the orientations nor the locations of the jigsaw pieces are known. This hard combinatorial sorting task is formulated as a nonconvex quadratic programming problem that is solved via the projected power method. Specifically, this work aims to specify the locations and orientations of puzzle pieces by maximizing a constrained quadratic function that resolves an optimized permutation matrix composed of the noisy pairwise affinities between jigsaw pieces. The experimental results obtained in the MIT, McGill and Pomeranz datasets indicate that our method outperforms state-of-the-art techniques.

Automated Screening of COVID-19 from chest CT is of emergency and importance during the outbreak of SARS-CoV-2 worldwide in 2020. However, accurate screening of COVID-19 is still a massive challenge due to the spatial complexity of 3D volumes, the labeling difficulty of infection areas, and the slight discrepancy between COVID-19 and other viral pneumonia in chest CT. While a few pioneering works have made significant progress, they are either demanding manual annotations of infection areas or lack of interpretability. In this paper, we report our attempt towards achieving highly accurate and interpretable screening of COVID-19 from chest CT with weak labels. We propose an attention-based deep 3D multiple instance learning (AD3D-MIL) where a patient-level label is assigned to a 3D chest CT that is viewed as a bag of instances. AD3D-MIL can semantically generate deep 3D instances following the possible infection area. AD3D-MIL further applies an attention-based pooling approach to 3D instances to provide insight into each instance's contribution to the bag label. AD3D-MIL finally learns Bernoulli distributions of the bag-level labels for more accessible learning. We collected 460 chest CT examples: 230 CT examples from 79 patients with COVID-19, 100 CT examples from 100 patients with common pneumonia, and 130 CT examples from 130 people without pneumonia. A series of empirical studies show that our algorithm achieves an overall accuracy of 97.9%, AUC of 99.0%, and Cohen kappa score of 95.7%. These advantages endow our algorithm as an efficient assisted tool in the screening of COVID-19.