This study proposes a novel method for extracting breast cancer tumor heterogeneity descriptors to non-invasively predict whether pathological complete response (pCR) can be achieved after neoadjuvant chemotherapy (NAC). These localized descriptors extract corresponding heterogeneity features for different radiomic features and are able to capture tumor characteristics at various localization levels. These descriptors also capture tumor heterogeneity both at the individual tumor level and across the whole dataset, providing decision-making models with features that are both more effective and interpretable. We validated the effectiveness of the proposed features with the Kolmogorov-Arnold network (KAN) across multiple centers, yielding an AUC of 0.92 when combined with pathological features and demonstrating good performance in external datasets (AUCs of 0.84 and 0.81). Additionally, we transform the best model into a symbolic formula to intuitively explain the machine learning model's prediction process, showing how factors such as age, HER2, Ki-67 and heterogeneity influence the prediction. The symbolized model is consistent with the experience of clinical experts, which enhances users' confidence in deep models. The experimental results show that our proposed features and method outperform classical heterogeneity features and end-to-end neural networks with a small additional computational cost.

Acute appendicitis (AA) is one of the most prevalent surgical acute abdominal condition diseases. The recognition and segmentation of the inflamed appendix are important for AA diagnosis. However, it is a challenging task to find and segment the inflamed appendix from computed tomography (CT) images due to the varying sizes and shapes of different appendices and blurred borders with nearby tissues. To the best of our knowledge, the general expert segmentation model suffers due to the characterization of the inflamed appendix. Thus, we propose a deformable convolutional encoder-decoder network (DCED) for better recognition and segmentation of the inflamed appendix. The network consists of an encoder, a bottleneck, and a decoder. The encoder is composed of several convolutional neural network (CNN) layers to capture the local structural information. The bottleneck based on a vision transformer (ViT) focuses on the region of interest (ROI) using the global attention mechanism. The encoder and bottleneck modules effectively combine the local and global information of input data to locate the inflamed appendix. The decoder based on a deformable convolutional network (DCN) learns the varied boundary information, which helps to improve the accuracy of boundary segmentation. Extensive experimental results on a real-world AA dataset show that the proposed method yields the best average Dice similarity coefficient (DSC) of 71.29% and average Hausdorff Distance 95% (HD95) of 12.38 mm in comparison to state-of-the-art segmentation methods.

In this work, we propose a radiomics-guided neural network, XRadNet, for breast cancer molecular subtype prediction. XRadNet is a two-head neural network, with one for predicting molecular subtypes and the other for approximating radiomic features. In addition, a training scheme with radiomics guidance is proposed to improve performance. First, we conduct a series of experiments to test the radiomic feature learning capacity of different neural networks, which determines the backbone of XRadNet. Moreover, significant radiomic features are also determined according to radiomics and prior knowledge. XRadNet is subsequently pretrained in a self-supervised manner. The pretraining uses synthetic samples to train the backbone and radiomic feature regression head. This mitigates the impact of an insufficient number of samples. Finally, XRadNet is fine-tuned with a downstream real-world dataset by enabling all heads. Furthermore, a logistic regression is built with radiomic features and learned features, which provides a new way to interpreting the trained model with concepts familiar to radiologists. The experimental results show that XRadNet effectively predicts the four molecular subtypes of breast cancer. These results also demonstrate that the proposed training scheme yields better or competitive performance than those models pretrained on ImageNet or medical datasets.

When analyzing CT images of patients with suspected appendicitis, radiologists need to observe and examine consecutive 2D CT slices. Computer-assisted detection of the appendix in 2D CT slices significantly improve the diagnostic efficiency of radiologists. However, existing 2D medical image object detection methods primarily focus on spatial features within a single CT slice, which overlook spatial relationships between consecutive slices. We propose an Appendix Object Correction Network (AOCN) to refine predictions of universal object detectors. Although AOCN is a 2D network, it effectively leverages spatial relationships across consecutive CT slices. AOCN requires only a few training epochs to improve the accuracy of bounding boxes significantly, which offers advantages such as high scalability, low cost, and reduced training time. It consists of a global case feature learning module for extracting global feature map from the CT case and an object feature relation module for modeling the relationships between objects across slices. Experimental results demonstrate the effectiveness and efficiency of AOCN in correcting the output bounding boxes of several mainstream object detection networks, with a 6% to 14% improvement in Recall while requiring only a few training epochs.

Breast cancer is a heterogeneous disease, where molecular subtypes of breast cancer are closely related to the treatment and prognosis. Therefore, the goal of this work is to differentiate between luminal and non-luminal subtypes of breast cancer. The hierarchical radiomics network (HRadNet) is proposed for breast cancer molecular subtypes prediction based on dynamic contrast-enhanced magnetic resonance imaging. HRadNet fuses multilayer features with the metadata of images to take advantage of conventional radiomics methods and general convolutional neural networks. A two-stage training mechanism is adopted to improve the generalization capability of the network for multicenter breast cancer data. The ablation study shows the effectiveness of each component of HRadNet. Furthermore, the influence of features from different layers and metadata fusion are also analyzed. It reveals that selecting certain layers of features for a specified domain can make further performance improvements. Experimental results on three data sets from different devices demonstrate the effectiveness of the proposed network. HRadNet also has good performance when transferring to other domains without fine-tuning.

Acute appendicitis (AA) is one of the most prevalent surgical acute abdominal condition diseases. The treatment management of A A is highly dependent on the CT image diagnosis. However, the in-flamed appendix exhibits blurred boundaries with nearby tissue, varying shapes, and sizes. These properties require high robustness and generalization capability of inflamed appendix segmentation networks. In this paper, we propose a CNN-Transformer-based encoder-decoder segmentation network (LSSED) equipped with localized stochastic sensitivity (LSS) loss function and residual dilated paths (RD-Paths) to solve above problems. The proposed method effectively learns robust features of the input data by reducing the LSS of unseen samples. In addition, the RD-Paths capture multiscale feature information and reduce the semantic gap between the encoder and decoder, which improves the accuracy of the segmentation. Empirical studies on a real-world AA dataset show that our method yields the best performance in terms of average Dice similarity coefficient (DSC) and Hausdorff Distance of 95% (HD95) compared to several state-of-the-art segmentation networks.

Joint angle error of robotic arm has great impacts on the accuracy of the end-effector, which is critical in industrial applications. Therefore, in this article, an online cognitive joint angle error compensation method based on incremental learning is proposed to reduce joint angle error. The proposed method consists of a joint angle error solver and a compensation module, which ensure that the robot can obtain effective joint angle compensation in various situations. The joint angle error solver is used to solve joint angle error online. It uses the redundant constraint method for multilink position measurement so as to calculate the position error of the robot accurately later. The compensation module uses the self-feedback incremental fuzzy neural network (SFIFN) to predict and update the compensation in real time. SFIFN is a variant of the fuzzy neural network (FNN), which uses long short-term memory to introduce a feedback mechanism based on FNN. The incremental learning capability of SFIFN reduces the time for solving error and makes the module runs in real time. Specifically, two inertial measurement units mounted at the ends of links are used to measure pose changes of the ends of corresponding links. Both the simulated and the real experiments show that the proposed method yields good compensations to joint angle error and its potentials for smart manipulation.