Knowledge distillation can mitigate catastrophic forgetting in domain continual segmentation by transferring knowledge from the older model to the newer model. However, existing distillation-based methods primarily emphasize knowledge retention while overlooking inherent defects in the older teacher models. As a result, these teacher-originated defects, such as knowledge gaps or biases, are propagated and exacerbate forgetting. To address this challenge, we propose a Targeted Knowledge Rectification Learning framework (TKRL) to probe and correct teacher-originated defects. TKRL consists of two modules: 1) Probe-augmented Class Distillation, which generates gradient-driven "probes" to uncover underrepresented features in the older model, thereby bridging knowledge gaps by distilling hidden information into the new model; 2) Variance-guided Masked Autoencoder, which selectively masks and reconstructs critical high-uncertainty patches across multi-level semantic regions, thereby correcting biases inherited from the older model. Our experimental results show that TKRL effectively rectifies knowledge gaps and biases, thereby mitigating catastrophic forgetting and enhancing performance in domain continual segmentation.

Recently, self-training strategies have shown outstanding performance in the unsupervised medical image registration field. These strategies use their own network to generate pseudo-displacement fields (PFs) to supervise network training. However, limited diversity and accuracy of these PFs hinder their effectiveness. To address these limitations, we propose a novel mutual learning registration paradigm (MutualReg), where knowledge is distilled mutually between teacher and student networks for alternate improvement via recursive training. This involves two fundamental challenges: 1) how to generate more diverse and accurate PFs; and 2) how to effectively integrate knowledge distillation from the teacher network and learning from the student network. For the former, we employ a different and powerful teacher network thanks to the decoupling nature of MutualReg. For the latter, we introduce a Voxel-wise Reliability Criterion (VRC) module to retain reliable voxel locations of knowledge distillation. In the abdominal CT registration task, MutualReg outperforms state-of-the-art competitors, demonstrating its effectiveness. Code is available from https://github.com/PerceptionComputingLab/MutualReg/.

Despite the extensive utilization of deep learning in medical image segmentation, the achieved accuracy remains inadequate for clinical requirements due to the scarcity of annotated data, which constrains the acquisition of anatomical knowledge. Leveraging anatomical information is particularly advantageous in medical image segmentation, especially for multi-modal and cross-domain tasks. To better capture and represent anatomical structures, we propose a Swin Transformer-based anatomical structure-aware network, AnatSwin, which adopts a unique approach by utilizing label images as inputs. Compared with gray-scale images, label images, devoid of intensity information, explicitly enhance the representation of anatomical shape and spatial tissue relationships, offering valuable resources for learning anatomical structures effectively and allowing the model to concentrate on understanding morphological and spatial relationship cues. AnatSwin follows an encoder–decoder architecture, where the encoder incorporates two branches that share weights. The Swin-Transformer block serves as the basic unit of the encoder, accepting both the template label (representing the correct anatomical structure) and the pseudo label (generated by a registration model) as inputs. In order to facilitate efficient interaction among features at the same hierarchy, an attention-based feature interaction (FI) block is introduced. FI block enhances the model’s ability to capture anatomical structure by promoting feature interactions within the two branches. Furthermore, the decoder employs FI blocks to learn relationships between features at the same hierarchy, ultimately improving the segmentation performance. Experimental evaluations demonstrate that the proposed AnatSwin outperforms state-of-the-art models, highlighting its significant potential in improving the learning and representation of anatomical structures, as well as optimizing tasks related to medical image segmentation. This work signifies a promising step forward in addressing the challenges of medical image segmentation and paves the way for further advancements in the field.

Patients with myocardial ischemia and infarction are at increased risk of arrhythmias, which in turn, can exacerbate the overall risk of mortality. Despite the observed reduction in recurrent arrhythmias through antiarrhythmic drug therapy, the precise mechanisms underlying their effectiveness in treating ischemic heart disease remain unclear. Moreover, there is a lack of specialized drugs designed explicitly for the treatment of myocardial ischemic arrhythmia. This study employs an electrophysiological simulation approach to investigate the potential antiarrhythmic effects and underlying mechanisms of various pharmacological agents in the context of ischemia and myocardial infarction (MI). Based on physiological experimental data, computational models are developed to simulate the effects of a series of pharmacological agents (amiodarone, telmisartan, E-4031, chromanol 293B, and glibenclamide) on cellular electrophysiology and utilized to further evaluate their antiarrhythmic effectiveness during ischemia. On 2D and 3D tissues with multiple pathological conditions, the simulation results indicate that the antiarrhythmic effect of glibenclamide is primarily attributed to the suppression of efflux of potassium ion to facilitate the restitution of [K+]o, as opposed to recovery of IKATP during myocardial ischemia. This discovery implies that, during acute cardiac ischemia, pro-arrhythmogenic alterations in cardiac tissue's excitability and conduction properties are more significantly influenced by electrophysiological changes in the depolarization rate, as opposed to variations in the action potential duration (APD). These findings offer specific insights into potentially effective targets for investigating ischemic arrhythmias, providing significant guidance for clinical interventions in acute coronary syndrome.

The shortage of annotated ECG data presents a significant impediment, hampering the overall generalization capabilities of machine learning models tailored for automated ECG classification. The collective integration of multisource datasets presents a potential remedy for this challenge. However, it is crucial to underscore that the mere addition of supplementary data does not automatically guarantee performance enhancement, given the unresolved challenges associated with multisource data. In this research, we address one such challenge, namely, the issue of incomplete labels arising from the diversity of annotations within multi-source ECG datasets. First, we identified three distinct types of label missing: dataset-related label missing, supertype missing, and subtype missing. To address the supertype missing effectively, we introduce a novel approach known as offline category mapping which leverages the hierarchical relationships inherent within the categories to recover the missing supertype labels. Additionally, two complementary strategies, referred to as prediction masking and online category mapping, are proposed to mitigating the adverse effects of subtype and dataset-related label missing on model optimization. These strategies enhance the model's ability to identify missing subtypes under conditions of weak supervision. These pioneering methodologies are integrated into a deep learning-based framework designed for multilabel ECG classification. The performance of our proposed framework is rigorously evaluated using realistic multi-source datasets obtained from the PhysioNet/CinC challenge 2020/2021. The proposed learning framework exhibits a notable improvement in macro-average precision, surpassing the corresponding baseline model by more than 25 % on the test datasets. As a result, this research study makes a substantial contribution to the field of ECG classification by addressing the critical issue of incomplete labels in multisource datasets, ultimately enhancing the generalization capabilities of machine learning models in this domain.

Accurate prediction of Drug-Target binding Affinity (DTA) is a daunting yet pivotal task in the sphere of drug discovery. Over the years, a plethora of deep learning-based DTA models have emerged, rendering promising results in predicting the binding affinities between drugs and their target proteins. However, in contrast to the conventional approach of modeling binding affinity in vector spaces, we propose a more nuanced modeling process in a continuous space to account for the diversity of input samples. Initially, the drug is encoded using the Simplified Molecular Input Line Entry System (SMILES), while the target sequences are characterized via a pretrained language model. Subsequently, highly correlative information is extracted utilizing residual gated convolutional neural networks. In a departure from existing deep learning-based models, our model learns the hidden representations of the drugs and targets jointly. Instead of employing two vectors, our hidden representations consist of two Gaussian distributions. To validate the effectiveness of our proposal, we conducted evaluations on commonly utilized benchmark datasets. The experimental outcomes corroborated that our method surpasses the state-of-the-art vectorial representation methods in terms of performance. This approach, therefore, offers potential enhancements in the precision of DTA predictions, potentially contributing to more efficient drug discovery processes.

OBJECTIVE: Detecting changes in the QRS complexes in ECG signals is regarded as a straightforward, noninvasive, inexpensive, and preliminary diagnosis approach for evaluating the cardiac health of patients. Therefore, detecting QRS complexes in ECG signals must be accurate over short times. However, the reliability of automatic QRS detection is restricted by all kinds of noise and complex signal morphologies. The objective of this paper is to address automatic detection of QRS complexes. METHODS: In this paper, we proposed a new algorithm for automatic detection of QRS complexes using dual channels based on U-Net and bidirectional long short-term memory. First, a proposed preprocessor with mean filtering and discrete wavelet transform was initially applied to remove different types of noise. Next the signal was transformed and annotations were relabeled. Finally, a method combining U-Net and bidirectional long short-term memory with dual channels was used for the automatic detection of QRS complexes. RESULTS: The proposed algorithm was trained and tested using 44 ECG records from the MIT-BIH arrhythmia database and CPSC2019 dataset, which achieved 99.06% and 95.13% for sensitivity, 99.22% and 82.03% for positive predictivity, and 98.29% and 78.73% accuracy on the two datasets respectively. CONCLUSION: Experimental results prove that the proposed method may be useful for automatic detection of QRS complex task. SIGNIFICANCE: The proposed method not only has application potential for QRS complex detecting for large ECG data, but also can be extended to other medical signal research fields.

Heart failure (HF) is associated with an increased propensity for atrial fibrillation (AF), causing higher mortality than AF or HF alone. It is hypothesized that HF-induced remodelling of atrial cellular and tissue properties promotes the genesis of atrial action potential (AP) alternans and conduction alternans that perpetuate AF. However, the mechanism underlying the increased susceptibility to atrial alternans in HF remains incompletely elucidated. In this study, we investigated the effects of how HF-induced atrial cellular electrophysiological (with prolonged AP duration) and tissue structural (reduced cell-to-cell coupling caused by atrial fibrosis) remodelling can have an effect on the generation of atrial AP alternans and their conduction at the cellular and one-dimensional (1D) tissue levels. Simulation results showed that HF-induced atrial electrical remodelling prolonged AP duration, which was accompanied by an increased sarcoplasmic reticulum (SR) Ca2+ content and Ca2+ transient amplitude. Further analysis demonstrated that HF-induced atrial electrical remodelling increased susceptibility to atrial alternans mainly due to the increased sarcoplasmic reticulum Ca2+-ATPase (SERCA) Ca2+ reuptake, modulated by increased phospholamban (PLB) phosphorylation, and the decreased transient outward K+ current (Ito). The underlying mechanism has been suggested that the increased SR Ca2+ content and prolonged AP did not fully recover to their previous levels at the end of diastole, resulting in a smaller SR Ca2+ release and AP in the next beat. These produced Ca2+ transient alternans and AP alternans, and further caused AP alternans and Ca2+ transient alternans through Ca2+→AP coupling and AP→Ca2+ coupling, respectively. Simulation of a 1D tissue model showed that the combined action of HF-induced ion channel remodelling and a decrease in cell-to-cell coupling due to fibrosis increased the heart tissue's susceptibility to the formation of spatially discordant alternans, resulting in an increased functional AP propagation dispersion, which is pro-arrhythmic. These findings provide insights into how HF promotes atrial arrhythmia in association with atrial alternans.

Due to bone deformation and joint space narrowing in diseased hips, accurate segmentation for pelvis, and femur from hip computed tomography (CT) images remains a challenging task. Therefore, the paper presents a fully automatic segmentation framework for the pelvis and femur in both of healthy and diseased hips. The framework involves three steps: preprocessing, coarse segmentation, and refinement. It starts with a preprocessing procedure to extract the volume of interest (VOI) from original CT images. Then, a coarse segmentation of bone has been obtained by classifying the VOI as bone and nonbone parts based on conditional random field (CRF) model. Finally, the bone is further divided into the pelvis and femur using a patch-based refinement method. The innovation of this study is the novel patch-based refinement method that is particularly suitable for diseased hips. The refinement method starts from the boundary of coarse segmentation, and propagates to the neighbors only when the label is not consistent with the label of CRF-based classification, it increases the reliability of segmentation for diseased hips with bone deformation. We incorporate neighborhood information to label fusion so that final label estimation is more accurate and robust for diseased hips with joint space narrowing. In total, 60 CT data sets, which included 78 healthy hemi-hips and 42 diseased hemi-hips, were used, and three-fold cross validations were carried out. Compared to two state-of-the-art methods, our method achieved significantly increased segmentation accuracy for the diseased hemi-hips, and is, therefore, more suited for automatic segmentation of diseased hips.

In order to maximize the motivation of students in the programming practice, this paper offers an analysis on the core factors of practice case motivating students put in effort in programming practice, namely, "interest", "usability", and "hierarchy". Furthermore, we present typical practice cases which are carefully designed according to the motivating factors and give a description on the implementation and experience of our programming practice course at Harbin Institute of Technology. The designed programming practice can not only train the students' practical programming skills but also enhance their self-regulated learning skills, creativity and self-efficacy.

We introduce a new belief revision operator called evaluative multiple revision. Belief states in the revision are belief bases with core beliefs. New information that triggers the revision is evaluated beliefs, some of which are evaluated by the core beliefs as plausible and the others implausible. We characterize this operator by axiomatic postulates in the AGM (named after the three authors, i. e. C.E. Alchourrón, P. Gärdenfors, and D. Makinson, in literature of belief revision) style. Two functional constructions are given for the operator based on evaluative kernel sets and evaluative remainder sets, respectively, with representation theorems proved. We also compare some related works with ours and show the generality of the operator.

Cisapride as a prokinetic drug inhibits rapid delayed rectifier potassium channel current. As producing QT interval prolongation and causes fatal cardiac arrhythmias, it has been withdrawn from clinical uses. However, exact mechanisms for the proarrhythmic effects of cisapride are incompletely unclear. In this study, we implemented a biophysically detailed computational model of the heart to quantify the effects of the cisapride on cardiac electrical activities at cellular and tissue levels, from which we analyzed the proarrhythmic effects of the agent.

Turning non-autonomic ventricular cells into pacemaking cells is believed to hold the key for making a bio-pacemaker that could potentially treat patients with cardiac conduction diseases. In this article, we analyze the effects of various membrane ion channel currents on ventricular automaticity induced by reducing the inward-rectifier K+current (IK1). It was found that the L-type calcium current (ICaL), rather than the fast sodium current (INa), plays a major role in the rapid depolarization phase of the action potential. With a small ICaL, the automaticity of cells failed due to incompletion of the rapid depolarization. However, during the slow depolarization phase of the action potential, the background sodium current (IbNa), background calcium current (IbCa) and Na+/Ca2+exchanger current (INaCa) were playing more important roles. In 2D simulations, the automatic ventricular excitations arising from IK1reduction only couldn't propagate; it required other currents to be modulated at the same time for driving the surrounding cardiac tissues.

Hodgkin-Huxley(HH) equation is a classical model in electrophysiology and has been studied by many scholars. Applying stability theory, and taking maximal sodium conductance g̅naand potassium conductance g̅kas variables, in this study we analyze the stability and bifurcations of the model. Bifurcations are found when the variables change, and bifurcation points and boundary are calculated. When g̅nais the variable, there is only one bifurcation point and there are two points when g̅kis variable. The (g̅na, g̅k) plane is partitioned into two regions and the upper bifurcation boundary is similar to a line when both g̅naand g̅kare variables. The results gotten could be a help to control relevant diseases caused by maximal conductance anomaly.