Electrocardiogram (ECG) signals are crucial indicators of various human physiological states. A thorough analysis of these signals is indispensable for applications such as disease prediction, mental stress assessment, and other medical diagnostics. Despite the rapid progress in large language models (LLM) and their demonstrated prowess in natural language understanding, their application in ECG signal analysis remains underexplored. This paper introduces Tackle Electrocardiogram with Large Language Model Effectively (TELL ME), a novel approach that effectively transfers the robust comprehension capabilities of LLM to ECG signal processing. The method employs a front alignment strategy to align ECG modality with text modality via cross-attention mechanism and incorporates critical manual features into prompts to enhance the performances in specific tasks. The proposed solution has been validated across three downstream tasks, namely, quality assessment, ventricular premature beats detection, and denoising reconstruction, consistently achieving state-of-the-art (SOTA) results.

Abstract To obtain better spectral efficiency and higher bit rate, a novel carrier index M‐ary differential chaos shift keying modulation scheme is proposed. In this system, part of subcarriers is assigned to the chaotic reference so that it can carry extra data bits through carrier index modulation, while one of the remaining subcarriers is activated to transmit the data‐bearing signal. In addition, M‐ary DCSK modulation is also applied to data‐bearing signals based on the constellation theory and Walsh codes. Theoretical bit error rate expressions are derived over the multipath Rayleigh fading and additive white Gaussian noise channels. Simulations and comparisons are performed with various combinations of chaotic sequence lengths, subcarrier numbers and constellation sizes. Results show that the proposed scheme can outperform other counterparts in both spectral efficiency and bit error rate performances.

This article considers the issue of adaptive fuzzy predefined-time control for nonlinear systems. Fuzzy logic systems (FLSs) are introduced to estimate the uncertain nonlinear functions. The command filter technology is applied to overcome the difficulty of “explosion of complexity”. The error compensation mechanism is adopted to compensate the error generated via command filter. Based on the backstepping technique, a fuzzy adaptive predefined-time command filter control scheme is presented. The presented control strategy demonstrates that all the signals in closed-loop system are bounded and the tracking error can converge to a small area near zero within predefined time. The simulation results illustrate the validity of the developed control strategy.

OBJECTIVE: Nuclei segmentation is a crucial pre-task for pathological microenvironment quantification. However, the acquisition of manually precise nuclei annotations for improving the performance of deep learning models is time-consuming and expensive. METHODS: In this paper, an efficient nuclear annotation tool called NuSEA is proposed to achieve accurate nucleus segmentation, where a simple but effective ellipse annotation is applied. Specifically, the core network U-Light of NuSEA is lightweight with only 0.86 M parameters, which is suitable for real-time nuclei segmentation. In addition, an Elliptical Field Loss and a Texture Loss are proposed to enhance the edge segmentation and constrain the smoothness simultaneously. RESULTS: Extensive experiments on three public datasets (MoNuSeg, CPM-17, and CoNSeP) demonstrate that NuSEA is superior to the state-of-the-art (SOTA) methods and better than existing algorithms based on point, rectangle, and text annotations. CONCLUSIONS: With the assistance of NuSEA, a new dataset called NuSEA-dataset v1.0, encompassing 118,857 annotated nuclei from the whole-slide images of 12 organs is released. SIGNIFICANCE: NuSEA provides a rapid and effective annotation tool for nuclei in histopathological images, benefiting future explorations in deep learning algorithms.

Deep convolution neural networks have been widely used in medical image analysis, such as lesion identification in whole-slide images, cancer detection, and cell segmentation, etc. However, it is often inevitable that researchers try their best to refine annotations so as to enhance the model performance, especially for cell segmentation task. Weakly supervised learning can greatly reduce the workload of annotations, while there is still a huge performance gap between the weakly and fully supervised learning approaches. In this work, we propose a weakly-supervised cell segmentation method, namely Multi-Task Cell Segmentation Network (MTCSNet), for multi-modal medical images, including pathological, brightfield, fluorescent, phase-contrast and differential interference contrast images. MTCSNet is learnt in a single-stage training manner, where only two annotated points for each cell provide supervision information, and the first one is the centroid, the second one is its boundary. Additionally, five auxiliary tasks are elaborately designed to train the network, including two pixel-level classifications, a pixel-level regression, a local temperature scaling and an instance-level distance regression task, which is proposed to regress the distances between the cell centroid and its boundaries in eight orientations. The experimental results indicate that our method outperforms all state-of-the-art weakly-supervised cell segmentation approaches on public multi-modal medical image datasets. The promising performance also shows that a single-stage learning with two-point labeling approach are sufficient for cell segmentation, instead of fine contour delineation. The codes are available at: https://github.com/binging512/MTCSNet.

OBJECTIVE: Electrocardiogram (ECG) signals have wide-ranging applications in various fields, and thus it is crucial to identify clean ECG signals under different sensors and collection scenarios. Despite the availability of a variety of deep learning algorithms for ECG quality assessment, these methods still lack generalization across different datasets, hindering their widespread use. METHODS: In this paper, an effective model named Swin Denoising AutoEncoder (SwinDAE) is proposed. Specifically, SwinDAE uses a DAE as the basic architecture, and incorporates a 1D Swin Transformer during the feature learning stage of the encoder and decoder. SwinDAE was first pre-trained on the public PTB-XL dataset after data augmentation, with the supervision of signal reconstruction loss and quality assessment loss. Specially, the waveform component localization loss is proposed in this paper and used for joint supervision, guiding the model to learn key information of signals. The model was then fine-tuned on the finely annotated BUT QDB dataset for quality assessment. RESULTS: SwinDAE achieved 0.02-0.13 mean F1 score improvement on the BUT QDB dataset compared to multiple deep learning methods, and demonstrated applicability on two other datasets. CONCLUSION: The proposed SwinDAE shows strong generalization ability on different datasets, and surpasses other state-of-the-art deep learning methods on multiple evaluation metrics. In addition, the statistical analysis for SwinDAE prove the significance of the performance and the rationality of the prediction. SIGNIFICANCE: SwinDAE can learn the commonality between high-quality ECG signals, exhibiting excellent performance in the application of cross-sensors and cross-collection scenarios.

Research on automated diagnosis of Coronavirus Disease 2019 (COVID-19) has increased in recent months. SPGC COVID19 aims at classifying the grouped images of the same patient into COVID, Community Acquired Pneumonia(CAP) or normal. In this paper, we propose a novel ensemble learning framework to solve this problem. Moreover, adaptive boosting and dataset clustering algorithms are introduced to improve the classification performance. In our experiments, we demonstrate that our framework is superior to existing networks in terms of both accuracy and sensitivity.

Compared with abundant application of deep learning on hematoxylin and eosin (H&E) images, the study on immunohistochemical (IHC) images is almost blank, while the diagnosis of chronic endometritis mainly relies on the detection of plasma cells in IHC images. In this paper, a novel framework named Global Scanner with Local Discriminator (GSLD) is proposed to detect plasma cells with highly sparse distribution in IHC whole slide images (WSI) effectively and efficiently. Firstly, input an IHC image, the Global Scanner subnetwork (GSNet) predicts a distribution map, where the candidate plasma cells are localized quickly. Secondly, based on the distribution map, the Local Discriminator subnetwork (LDNet)discriminates true plasma cells by adopting only local information, which greatly speeds up the detection. Moreover, a novel grid-oversampling strategy for WSI preprocessing is proposed to relieve sample imbalance problem. Experimentas show that the proposed framework outperforms the representative object detection networks in both speed and accuracy.

OBJECTIVE: Cervical cancer, as one of the most frequently diagnosed cancers in women, is curable when detected early. However, automated algorithms for cervical pathology precancerous diagnosis are limited. METHODS: In this paper, instead of popular patch-wise classification, an end-to-end patch-wise segmentation algorithm is proposed to focus on the spatial structure changes of pathological tissues. Specifically, a triple up-sampling segmentation network (TriUpSegNet) is constructed to aggregate spatial information. Second, a distribution consistency loss (DC-loss) is designed to constrain the model to fit the inter-class relationship of the cervix. Third, the Gauss-like weighted post-processing is employed to reduce patch stitching deviation and noise. RESULTS: The algorithm is evaluated on three challenging and public datasets: 1) MTCHI for cervical precancerous diagnosis, 2) DigestPath for colon cancer, and 3) PAIP for liver cancer. The Dice coefficient is 0.7413 on the MTCHI dataset, which is significantly higher than the published state-of-the-art results. CONCLUSION: Experiments on the public dataset MTCHI indicate the superiority of the proposed algorithm on cervical pathology precancerous diagnosis. In addition, the experiments on two other pathological datasets, i.e., DigestPath and PAIP, demonstrate the effectiveness and generalization ability of the TriUpSegNet and weighted post-processing on colon and liver cancers. SIGNIFICANCE: The end-to-end TriUpSegNet with DC-loss and weighted post-processing leads to improved segmentation in pathology of various cancers.

Cervical cancer, as one of the most frequently diagnosed cancers worldwide, is curable when detected early. Histopathology images play an important role in precision medicine of the cervical lesions. However, few computer aided algorithms have been explored on cervical histopathology images due to the lack of public datasets. In this article, we release a new cervical histopathology image dataset for automated precancerous diagnosis. Specifically, 100 slides from 71 patients are annotated by three independent pathologists. To show the difficulty of the task, benchmarks are obtained through both fully and weakly supervised learning. Extensive experiments based on typical classification and semantic segmentation networks are carried out to provide strong baselines. In particular, a strategy of assembling classification, segmentation, and pseudo-labeling is proposed to further improve the performance. The Dice coefficient reaches 0.7833, indicating the feasibility of computer aided diagnosis and the effectiveness of our weakly supervised ensemble algorithm. The dataset and evaluation codes are publicly available. To the best of our knowledge, it is the first public cervical histopathology dataset for automated precancerous segmentation. We believe that this work will attract researchers to explore novel algorithms on cervical automated diagnosis, thereby assisting doctors and patients clinically.

Cervical cancer is one of the most commonly diagnosed cancer types worldwide, while is curable if detected early. However, few computer-aided algorithms have been explored on cervical histology image, which is vital for abnormality assessment. In this paper, an end-to-end deep segmentation network for complex cervical histology images is proposed, and a benchmark evaluation is contributed. Specifically, we observe that four-category cervical histology images possess a progressive inter-class association. To model the relationship, inspired by the elasticity, an adaptive elastic loss is proposed to reduce the deviation between difficult samples and their true categories. Moreover, five evaluation metrics are designed to measure the segmentation performance, and the Window Precision is particularly valuable for the evaluation of semi-supervised algorithms due to its robustness to the mislabeling. Finally, on a cervical histology dataset, benchmark experiments based on deep networks are conducted, and the results demonstrate the superiority of our new loss.

The scarcity of professional doctors stimulates the progress of breast cancer classification. However, there are still numerous challenges such as varied appearances (color, texture etc.) of microscopy images and the ambiguous category boundaries. In this paper, we propose an efficient and effective method to achieve multi-classification for H&E stained breast cancer images. Firstly, to restrain color noises in the staining stage, data augmentation in HSV color space is used to increase the diversity of color distribution. In addition, inspired by the principle of gravitation, a Gravitation Loss (G-loss) is proposed to maximize inter-class difference and minimize intra-class variance. The experimental results on public BACH 2018 dataset indicate that the proposed algorithm achieves the state-of-the-art performance, which demonstrates its effectiveness.