Class incremental medical image segmentation (CIMIS) aims to preserve knowledge of previously learned classes while learning new ones without relying on old-class annotations. However, existing methods 1) either adopt one-size-fits-all strategies that treat all spatial regions and feature channels equally, which may hinder the preservation of accurate old knowledge, 2) or focus solely on aligning local prototypes with global ones for old classes while overlooking their local representations in new data, leading to knowledge degradation. To mitigate the above issues, we propose Prototype-Guided Calibration Distillation (PGCD) and Dual-Aligned Prototype Distillation (DAPD) for CIMIS in this paper. Specifically, PGCD exploits prototype-to-feature similarity to calibrate class-specific distillation intensity in different spatial regions, effectively reinforcing reliable old knowledge and suppressing misleading cues from old classes. Complementarily, DAPD aligns the local prototypes of old classes extracted from the current model with both global historical prototypes and local prototypes, further enhancing segmentation performance on old categories. Comprehensive evaluations on two widely used multi-organ segmentation benchmarks demonstrate that our method outperforms current state-of-the-art methods, highlighting its robustness and generalization capabilities.

Inverse treatment planning is pivotal in tumor treatment planning. It enables the multi-objective optimization of radiation dose delivery, ensuring precise tumor targeting while sparing surrounding healthy tissues. This process often requires frequent parameter adjustments to achieve the desired balance between objectives, making it both labor-intensive and time-consuming. Deep reinforcement learning (DRL) provides an automated, model-based planning solution, aimed at reducing reliance on human expertise and enhancing the efficiency of objective parameter optimization. However, most current approaches apply DRL to inverse planning without fully leveraging the knowledge embedded in the continuous state-action space, defined by the coupling between nonstationary planning states and continuous decision variables. This may result in insufficient exploration and exploitation, leading to inefficient optimization. This work introduces an innovative action-guided DRL (AgDRL) approach for automatic inverse planning. Our goal is to enhance exploration and exploitation by leveraging insightful guidance from reward-guided actions. The implementation of AgDRL incorporates both exploitation and exploration in the action-state space. For exploitation, high-reward actions are employed as guidance to achieve the optimal action adjustment. For exploration, low-reward actions are recommended as training resets to explore a broader range of the latent state space. Quantitative and qualitative experiments are conducted in various settings to evaluate the proposed method. The results are assessed using DRL-related metrics (e.g. reward gains) and clinical-related measurements (e.g. dose-volume histograms, DVHs). Experimental results on a real-world rectal cancer dataset empirically demonstrate that the proposed AgDRL-based approach significantly improves optimization efficiency through a high-reward strategy while enhancing exploration diversity via a low-reward strategy, consistently outperforming the MatRad treatment planning optimization platform.

Magnetic resonance imaging (MRI)-guided radiotherapy (MRIgRT) integrates MRI with linear accelerators (MRI-linacs), enabling real-time motion management based on temporally resolved 2D MRI (cine-MRI). Current systems rely on template matching or deformable image registration for radiotherapy target (typically the gross tumor volume) localization, which allows beam gating. Further advances in localization could support more precise and efficient delivery methods. https://trackrad2025.grand-challenge.org/ was organized to provide a common dataset to benchmark algorithms for MRIgRT target tracking in 2D+t cine-MRI. Participants propagated target segmentation masks from an initialization frame across subsequent frames. The dataset comprised sagittal cine-MRI scans of 585 cancer patients undergoing radiotherapy at 0.35 T and 1.5 T MRI-linacs at six different institutions, with expert-annotated targets in 108 sequences. Target sites included the thorax (179 cases), abdomen (266 cases), and pelvis (140 cases). A total of 477 unlabeled and 50 labeled cases were provided for training purposes, 58 cases were kept private for preliminary testing (8) and final evaluation (50). The algorithms submitted by participants were executed on the challenge platform and assessed using metrics in three categories: geometric accuracy, surrogate dose accuracy and execution speed. Rankings were derived via a Rank-Then-Mean scheme. TrackRAD2025 attracted 148 registrations from 28 countries, 100 preliminary submissions and 24 final submissions from 14 teams. The top five methods achieved mean Dice similarity coefficients >0.87 and Euclidean center distances <2.1 mm, comparable to interobserver variability. Leading top five solutions featured foundation models with (4) or without (1) finetuning. Field strength had minimal effect on performance and tracking worked better for the pelvis with reduced motion amplitude compared to the thorax and abdomen cases, which achieved equivalent performance. TrackRAD2025 established a benchmark for MRIgRT tracking on multi-institutional cine-MRI data, highlighting foundation models as promising for clinical translation.

Propagation-based methods have drawn increasing research attention in interactive medical image segmentation. However, existing propagation-based methods face two significant challenges: 1) Due tothe continuous nature of anatomical structures within the organs and tumors throughout the volume, over-propagation is likely to occur as the propagation process reaches the end of structures, leadingto a degradation in segmentation performance. 2) During the multi-round refinement process, selecting the worst-segmented slice for refinement tends to hinder the optimization of segmentation results. To overcome these challenges, we propose the Discrepancy Aware Network (DANet), which includes a Discrepancy Learning Module (DLM) and employs a confidence loss to achieve accurate segmentation. Specifically, DLM captures the temporal-contextual discrepancy between previous and current slices, enabling the model to perceive the variations of the target. Furthermore, the confidence loss is responsible for regularizing the over-confident segmentation at the image level by estimating the target foreground. Additionally, we design a straightforward slice selection strategy to optimize the refinement process. Extensive experimental results on five public medical datasets demonstrate significant improvements over state-of-the-art methods (e.g., with +1.07% improvement on the MSD-Spleen dataset).

Fluence map optimization problem (FMOP) refers to the optimization of the intensity of radiation beams, which is a crucial part of intensity-modulated radiation therapy (IMRT). The FMOP is often considered as a multi-objective optimization problem due to the numerous treatment objectives that need to be met. This paper formulates FMOP as an unconstrained two-objective optimization problem that focuses on dose-volume constraints and specifically designed a multi-objective coevolutionary optimization algorithm named MOEA/FMOP. The MOEA/FMOP utilizes a cooperative strategy to process dual populations with two different resolutions of fluence maps. One high-resolution population focuses on convergence and fine-tuning, while the other roughly encoded one serves to improve global convergence and maintain diversity. The resolutions of the populations are encoded and initialized according to the clinical methodology. In comparison to conventional MOEAs, MOEA/FMOP outperforms over five real-world cancer cases (including prostate, rectum, liver, nasopharynx and breast cases) in terms of hyper volume (HV) from the perspective of the performance indicator. Moreover, in the realm of clinical evaluation using dose-volume histograms (DVH), MOEA/FMOP exhibits a better capability to generate high-quality solutions concurrently.

Class incremental medical segmentation (CIMS) aims to sequentially learn new classes while preserving knowledge of previously learned categories in the absence of old-class labels. Current methods suffer from performance degradation under class imbalance and require additional segmentation heads to accommodate new categories. Inspired by recent prototype learning that leverages prototypes to achieve robust recognition of new categories under limited-data regimes, we introduce a Prototype-dRIven class increMEntal (PRIME) method. PRIME replaces the incremental segmentation heads with prototypes to mitigate class imbalance, allowing new class learning with the simple addition of new prototypes. Based on prototype learning, PRIME further involves three tailored techniques. First, prototype structure alignment imposes structural constraints on inter-prototype relations to maintain consistent relative distances in the feature space, improving the model's ability to distinguish distinct classes. Second, pixel-wise contrastive loss term groups embeddings of similar samples while separating those of different classes, enhancing segmentation accuracy across all categories. Finally, the consensus-based prototype update mechanism refines the old prototypes during the learning of new classes, preventing performance degradation on the old classes. Extensive experiments on two public multi-organ segmentation datasets demonstrate that our approach significantly outperforms state-of-the-art methods, validating the effectiveness of the proposed PRIME.

Radiation therapy is regarded as the mainstay treatment for cancer in clinic. Kilovoltage cone-beam CT (CBCT) images have been acquired for most treatment sites as the clinical routine for image-guided radiation therapy (IGRT). However, repeated CBCT scanning brings extra irradiation dose to the patients and decreases clinical efficiency. Sparse CBCT scanning is a possible solution to the problems mentioned above but at the cost of inferior image quality. To decrease the extra dose while maintaining the CBCT quality, deep learning (DL) methods are widely adopted. In this study, planning CT was used as prior information, and the corresponding strictly structure-preserved CBCT was simulated based on the attenuation information from the planning CT. We developed a hyper-resolution ultra-sparse-view CBCT reconstruction model, known as the planning CT-based strictly-structure-preserved neural network (PSSP-NET), using a generative adversarial network (GAN). This model utilized clinical CBCT projections with extremely low sampling rates for the rapid reconstruction of high-quality CBCT images, and its clinical performance was evaluated in head-and-neck cancer patients. Our experiments demonstrated enhanced performance and improved reconstruction speed.

Patient-specific quality assurance (QA) for Volumetric Modulated Arc Therapy (VMAT) plans is routinely performed in the clinical. However, it is labor-intensive and time-consuming for medical physicists. QA prediction models can address these shortcomings and improve efficiency. Current approaches mainly focus on single cancer and single modality data. They are not applicable to clinical practice. To assess the accuracy of QA results for VMAT plans, this paper presents a new model that learns complementary features from the multi-modal data to predict the gamma passing rate (GPR). According to the characteristics of VMAT plans, a feature-data fusion approach is designed to fuse the features of imaging and non-imaging information in the model. In this study, 690 VMAT plans are collected encompassing more than ten diseases. The model can accurately predict the most VMAT plans at all three gamma criteria: 2%/2 mm, 3%/2 mm and 3%/3 mm. The mean absolute error between the predicted and measured GPR is 2.17%, 1.16% and 0.71%, respectively. The maximum deviation between the predicted and measured GPR is 3.46%, 4.6%, 8.56%, respectively. The proposed model is effective, and the features of the two modalities significantly influence QA results.

With the increasing maturity of optics and photonics, hyperspectral technology has also greatly advanced. Hyperspectral images composed of hundreds of adjacent bands and containing useful information can be easily obtained. However, unlike ordinary remote sensing images, each sample in hyperspectral remote sensing images has high-dimensional features and contains rich spatial and spectral information, which greatly increases the difficulty of feature selection and mining, increases the computational complexity, and limits the recognition accuracy of the model. Therefore, in this letter, a novel hybrid dilated-convolution-guided feature filtering and enhancement strategy (HDCFE-Net) model is proposed to classify hyperspectral images. Dilated convolution can reduce the spatial feature loss without reducing the receptive field and can obtain distant features. It can also be combined with the traditional convolution without losing its original information. We propose a feature filtering and enhancement strategy that eliminates redundant features and reduces computational complexity. The core concept is to set a threshold feature value, like the rounding method, to filter and enhance features. Experiments on three well-known hyperspectral datasets—Indian Pines (IPs), Pavia University (PU), and Salinas—show that in less than 1% (IPs: 5%) of the training samples, the overall accuracy (OA) of our method is 77%, 89%, and 91%, respectively, which is superior to several well-known methods. The experiments demonstrated the effectiveness and superiority of HDCFE-Net.

Identifying position errors for Graves' ophthalmopathy (GO) patients using electronic portal imaging device (EPID) transmission fluence maps is helpful in monitoring treatment. However, most of the existing models only extract features from dose difference maps computed from EPID images, which do not fully characterize all information of the positional errors. In addition, the position error has a three-dimensional spatial nature, which has never been explored in previous work. To address the above problems, a deep neural network (DNN) model with structural similarity difference and orientation-based loss is proposed in this paper, which consists of a feature extraction network and a feature enhancement network. To capture more information, three types of Structural SIMilarity (SSIM) sub-index maps are computed to enhance the luminance, contrast, and structural features of EPID images, respectively. These maps and the dose difference maps are fed into different networks to extract radiomic features. To acquire spatial features of the position errors, an orientation-based loss function is proposed for optimal training. It makes the data distribution more consistent with the realistic 3D space by integrating the error deviations of the predicted values in the left-right, superior-inferior, anterior-posterior directions. Experimental results on a constructed dataset demonstrate the effectiveness of the proposed model, compared with other related models and existing state-of-the-art methods.

The challenge in fine-grained visual classification (FGVC) is that the similarity within intra-class may be larger than inter-class, where the discriminative details require more attention than traditional classification tasks. To generate channel-wise complementary and discriminative features in beneficial details of FGVC, we propose a multi-branch channel-wise enhancement network (MCEN), which includes multi-pattern spatial disruption mechanism, inter-channel complementarity module(ICM), and novel soft target loss. The raw images are scrambled in multi-pattern and then the sub-images with different degrees of confusion are combined into three pairs as inputs, where the scrambled operation can force the channel to look for the discriminative details. And ICM can measure the complementarity between key features and overall features to restrain the redundancy of features. The soft target loss is designed for classification and the semantic relationship between the blocks is learned to judge the degree of the chaos of the image. Our designed multi-branched structure utilizes the shallow visual and deep semantic features to judge the outcome jointly, where the image pairs obtained by segmentation and rearrangement are input into the different branches to extract more complementary features from different patterns of the raw image. Our method is trained end-to-end with only class labels. Experimental results show that our model outperforms the state-of-the-art performance on three fine-grained benchmarks.

With the development of information technology, thousands of devices are connected to the Internet, various types of data are accessed and transmitted through the network, which pose huge security threats while bringing convenience to people. In order to deal with security issues, many effective solutions have been given based on traditional machine learning. However, due to the characteristics of big data in cyber security, there exists a bottleneck for methods of traditional machine learning in improving security. Owning to the advantages of processing big data and high-dimensional data, new solutions for cyber security are provided based on deep learning. In this paper, the applications of deep learning are classified, analyzed and summarized in the field of cyber security, and the applications are compared between deep learning and traditional machine learning in the security field. The challenges and problems faced by deep learning in cyber security are analyzed and presented. The findings illustrate that deep learning has a better effect on some aspects of cyber security and should be considered as the first option.

To achieve high reliability in on-chip networks, it is necessary to test the network continuously with Built-in Self-Tests (BIST) so that the faults can be detected quickly and the number of affected packets can be minimized. However, BIST causes significant performance loss due to data dependencies. We propose EsyTest, a comprehensive test strategy with minimized influence on system performance. EsyTest tests the data path and the control path separately. The data path test starts periodically, but the actual test performs in the free time slots to avoid deactivating the router for testing. A reconfigurable router architecture and an adaptive fault-tolerant routing algorithm are proposed to guarantee the access to the processing core when the associated router is under test. During the whole test procedure of the network, all processing cores are accessible, and thus the system performance is maintained during the test. At the same time, EsyTest provides a full test coverage for the NoC and a better hardware compatibility comparing with the existing test strategies. Under the PARSEC benchmark and different test frequencies, the execution time increases less than 5 percent at the cost of 9.9 percent more area and 4.6 percent more power in comparison with the execution where no test procedure is applied.

To achieve high reliability in on-chip networks, frequent runs of Built-in Self-Test allow the detection of and recovery from faults before they affect packets and the system functionality. However, to test routers, wrappers isolate cores from the network which leads to execution blocking and performance loss. In this paper, we propose a design-for-test reconfigurable router with two alternative bypassing channels. The router architecture allows maintaining the connection between cores and the network during the testing procedure by utilizing the bypassing channels. With the help of an adaptive routing algorithm and a testing strategy, networks can be fully tested at a high testing frequency with <;15% increase of execution time.

To achieve high reliability in on-chip networks, it is necessary to test the network as frequently as possible to detect physical failures before they lead to system-level failures. A main obstacle is that the circuit under test has to be isolated, resulting in network cuts and packet blockage which limit the testing frequency. To address this issue, we propose a comprehensive network-level approach which could test multiple routers simultaneously at high speed without blocking or dropping packets. We first introduce a reconfigurable router architecture allowing the cores to keep their connections with the network while the routers are under test. A deadlock-free and highly adaptive routing algorithm is proposed to support reconfigurations for testing. In addition, a testing sequence is defined to allow testing multiple routers to avoid dropping of packets. A procedure is proposed to control the behavior of the affected packets during the transition of a router from the normal to the testing mode and vice versa. This approach neither interrupts the execution of applications nor has a significant impact on the execution time. Experiments with the PARSEC benchmarks on an 8x8 NoC-based chip multiprocessors show only 3 percent execution time increase with four routers simultaneously under test.

Faults may occur in numerous locations of a router in a NoC platform. Compared with the faults in the data path, faults in the control path may cause more severe effects which may result in crashing the entire system. Most of the current efforts in literature focus on disabling a router when a fault is detected. Considering this level of coarse-granularity, the functioning parts of a router have to be unnecessarily disabled which may severely affect the performance or functionality of the on-chip network. To cope with this problem, in this paper we propose a mechanism to tolerate faults in the control path which largely avoid disabling a router as long as the fault is not severe. This mechanism is called DMT, standing for three distinguishing characteristics of the proposed method as fault Detection, fault Masking and fault Tolerance. The proposed mechanism can efficiently detect the faults expressed as illegal turns while it has the capability to tolerate faults without a prior knowledge on where and why a fault has happened.

As technology scaling, reliability has became one of the key challenges of Network-on-Chip (NoC). Many faulttolerant routing algorithms for NoC are developed to overcome fault components and provide reliable transmission. But proposed routing algorithms do not pay enough attention to find the shortest paths, which increases latency and power consumption. In this paper, a fault-tolerant routing algorithm using new component states diffusion method based on Farthest Reachable Router (FRR) is proposed. This algorithm can reduce latency by finding the shortest paths between source and destination routers. Experiment results verify that FRR routing algorithm can tolerate 79% fault patterns within 3 × 3 and reduce latency by 16-44% compared with FON.

Distributed Antenna Systems (DAS) and femtocells are capable of improving the attainable performance in the cell-edge area and in indoor residential areas, respectively. In order to achieve a high spectral efficiency, both the Distributed Antenna Elements (DAEs) and Femtocell Base Stations (FBSs) may have to reuse the spectrum of the macrocellular network. As a result, the performance of both outdoor macrocell users and indoor femtocell users suffers from Co-Channel Interference (CCI). Hence in this paper, heterogenous cellular networks are investigated, where the DAS-aided macrocells and femtocells co-exist within the same area. The outage probability is derived and the network is optimised for minimising the outage probability. Our analysis demonstrates that surprisingly, the Unity Frequency Reuse (UFR) based macrocellular system can be optimised in isolation, without considering the impact of femtocells. We found that the macrocells relying on hard-FFR as well as on soft-FFR tend to migrate to several small cells, illuminated by the DAEs, when the density of femtocells becomes high.

Coalition Network Elements (CNE) are proposed for Base Stations (BS) cooperation, where the CNEs carry traffic for the BS in support of its cell-edge MSs by exploiting the unused frequency bands of the BS network, while considering a range of practical impairments. We derive the coalition probability by taking into account both system loads of the primary network as well as the CNE's greediness. Our simulation results demonstrate that the proposed solution is capable of substantially increasing the attainable SINR in a wide range of scenarios and it is also robust to diverse practical imperfections.

Cooperative Multiple Point (CoMP) transmission aided Distributed Antenna Systems (DAS) are proposed for increasing the received Signal-to-Interference-plus-Noise-Ratio (SINR) in the cell-edge area of a cellular system employing Fractional Frequency Reuse (FFR) in the presence of realistic imperfect Channel State Information (CSI) as well as synchronisation errors between the transmitters and the receivers. Our simulation results demonstrate that the CoMP aided DAS scenario is capable of increasing the attainable SINR by up to 3dB in the presence of a wide range of realistic imperfections.

As a potential future technology, impulse radio is a promising new reliable data transmission method for time-sensitive applications. However, owing to the complex nature of ultra-wideband (UWB) channels used in practice, there are many design problems that remain to be resolved. This study looks into some UWB channels classified as waveform distortion for which the authors present an implementation-oriented analysis to investigate the performance of impulse radio systems using antennas for a special class of deterministic multipath channels. This study analyses the waveform distortion along signal path to identify the effect of non-idealities in the system and evaluates overall performance of system in terms of bit error ratio observing the waveforms received at the correlator. Through simulation results practical aspects of the impulse-radio transceivers are discussed. From some of the results, the authors provide an antenna selection strategy for some propagation environments.