MutaPT: A Multi-Task Pre-Trained Transformer for Classifying State of Disorders of Consciousness Using EEG Signal.

Deep learning (DL) has been demonstrated to be a valuable tool for classifying state of disorders of consciousness (DOC) using EEG signals. However, the performance of the DL-based DOC state classification is often challenged by the limited size of EEG datasets. To overcome this issue, we introduce multiple open-source EEG datasets to increase data volume and train a novel multi-task pre-training Transformer model named MutaPT. Furthermore, we propose a cross-distribution self-supervised (CDS) pre-training strategy to enhance the model's generalization ability, addressing data distribution shifts across multiple datasets. An EEG dataset of DOC patients is used to validate the effectiveness of our methods for the task of classifying DOC states. Experimental results show the superiority of our MutaPT over several DL models for EEG classification.

Bayesian DivideMix++ for Enhanced Learning with Noisy Labels.

Leveraging inexpensive and human intervention-based annotating methodologies, such as crowdsourcing and web crawling, often leads to datasets with noisy labels. Noisy labels can have a detrimental impact on the performance and generalization of deep neural networks. Robust models that are able to handle and mitigate the effect of these noisy labels are thus essential. In this work, we explore the open challenges of neural network memorization and uncertainty in creating robust learning algorithms with noisy labels. To overcome them, we propose a novel framework called "Bayesian DivideMix++" with two critical components: (i) DivideMix++, to enhance the robustness against memorization and (ii) Monte-Carlo MixMatch, which focuses on improving the effectiveness towards label uncertainty. DivideMix++ improves the pipeline by integrating the warm-up and augmentation pipeline with self-supervised pre-training and dedicated different data augmentations for loss analysis and backpropagation. Monte-Carlo MixMatch leverages uncertainty measurements to mitigate the influence of uncertain samples by reducing their weight in the data augmentation MixMatch step. We validate our proposed pipeline using four datasets encompassing various synthetic and real-world noise settings. We demonstrate the effectiveness and merits of our proposed pipeline using extensive experiments. Bayesian DivideMix++ outperforms the state-of-the-art models by considerable differences in all experiments. Our findings underscore the potential of leveraging these modifications to enhance the performance and generalization of deep neural networks in practical scenarios.

Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center study.

Background: Prognosis is crucial for personalized treatment and surveillance suggestion of the resected non-small-cell lung cancer (NSCLC) patients in stage I-III. Although the tumor-node-metastasis (TNM) staging system is a powerful predictor, it is not perfect enough to accurately distinguish all the patients, especially within the same TNM stage. In this study, we developed an intelligent prognosis evaluation system (IPES) using pre-therapy CT images to assist the traditional TNM staging system for more accurate prognosis prediction of resected NSCLC patients. Methods: 20,333 CT images of 6371 patients from June 12, 2009 to March 24, 2022 in West China Hospital of Sichuan University, Mianzhu People's Hospital, Peking University People's Hospital, Chengdu Shangjin Nanfu Hospital and Guangan Peoples' Hospital were included in this retrospective study. We developed the IPES based on self-supervised pre-training and multi-task learning, which aimed to predict an overall survival (OS) risk for each patient. We further evaluated the prognostic accuracy of the IPES and its ability to stratify NSCLC patients with the same TNM stage and with the same EGFR genotype. Findings: The IPES was able to predict OS risk for stage I-III resected NSCLC patients in the training set (C-index 0.806; 95% CI: 0.744-0.846), internal validation set (0.783; 95% CI: 0.744-0.825) and external validation set (0.817; 95% CI: 0.786-0.849). In addition, IPES performed well in early-stage (stage I) and EGFR genotype prediction. Furthermore, by adopting IPES-based survival score (IPES-score), resected NSCLC patients in the same stage or with the same EGFR genotype could be divided into low- and high-risk subgroups with good and poor prognosis, respectively (p < 0.05 for all). Interpretation: The IPES provided a non-invasive way to obtain prognosis-related information from patients. The identification of IPES for resected NSCLC patients with low and high prognostic risk in the same TNM stage or with the same EGFR genotype suggests that IPES have potential to offer more personalized treatment and surveillance suggestion for NSCLC patients. Funding: This study was funded by the National Natural Science Foundation of China (grant 62272055, 92259303, 92059203), New Cornerstone Science Foundation through the XPLORER PRIZE, Young Elite Scientists Sponsorship Program by CAST (2021QNRC001), Clinical Medicine Plus X - Young Scholars Project, Peking University, the Fundamental Research Funds for the Central Universities (K.C.), Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences (2021RU002), BUPT Excellent Ph.D. Students Foundation (CX2022104).

Self-supervised enhanced thyroid nodule detection in ultrasound examination video sequences with multi-perspective evaluation.

Objective.Ultrasound is the most commonly used examination for the detection and identification of thyroid nodules. Since manual detection is time-consuming and subjective, attempts to introduce machine learning into this process are ongoing. However, the performance of these methods is limited by the low signal-to-noise ratio and tissue contrast of ultrasound images. To address these challenges, we extend thyroid nodule detection from image-based to video-based using the temporal context information in ultrasound videos.Approach.We propose a video-based deep learning model with adjacent frame perception (AFP) for accurate and real-time thyroid nodule detection. Compared to image-based methods, AFP can aggregate semantically similar contextual features in the video. Furthermore, considering the cost of medical image annotation for video-based models, a patch scale self-supervised model (PASS) is proposed. PASS is trained on unlabeled datasets to improve the performance of the AFP model without additional labelling costs.Main results.The PASS model is trained by 92 videos containing 23 773 frames, of which 60 annotated videos containing 16 694 frames were used to train and evaluate the AFP model. The evaluation is performed from the video, frame, nodule, and localization perspectives. In the evaluation of the localization perspective, we used the average precision metric with the intersection-over-union threshold set to 50% (AP@50), which is the area under the smoothed Precision-Recall curve. Our proposed AFP improved AP@50 from 0.256 to 0.390, while the PASS-enhanced AFP further improved the AP@50 to 0.425. AFP and PASS also improve the performance in the valuations of other perspectives based on the localization results.Significance.Our video-based model can mitigate the effects of low signal-to-noise ratio and tissue contrast in ultrasound images and enable the accurate detection of thyroid nodules in real-time. The evaluation from multiple perspectives of the ablation experiments demonstrates the effectiveness of our proposed AFP and PASS models.

Approaches to Improve the Quality of Person Re-Identification for Practical Use.

The idea of the person re-identification (Re-ID) task is to find the person depicted in the query image among other images obtained from different cameras. Algorithms solving this task have important practical applications, such as illegal action prevention and searching for missing persons through a smart city's video surveillance. In most of the papers devoted to the problem under consideration, the authors propose complex algorithms to achieve a better quality of person Re-ID. Some of these methods cannot be used in practice due to technical limitations. In this paper, we propose several approaches that can be used in almost all popular modern re-identification algorithms to improve the quality of the problem being solved and do not practically increase the computational complexity of algorithms. In real-world data, bad images can be fed into the input of the Re-ID algorithm; therefore, the new Filter Module is proposed in this paper, designed to pre-filter input data before feeding the data to the main re-identification algorithm. The Filter Module improves the quality of the baseline by 2.6% according to the Rank1 metric and 3.4% according to the mAP metric on the Market-1501 dataset. Furthermore, in this paper, a fully automated data collection strategy from surveillance cameras for self-supervised pre-training is proposed in order to increase the generality of neural networks on real-world data. The use of self-supervised pre-training on the data collected using the proposed strategy improves the quality of cross-domain upper-body Re-ID on the DukeMTMC-reID dataset by 1.0% according to the Rank1 and mAP metrics.