Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images

Chest imaging plays an essential role in diagnosing and predicting patients with COVID-19 with evidence of worsening respiratory status. Many deep learning-based approaches for pneumonia recognition have been developed to enable computer-aided diagnosis. However, the long training and inference time makes them inflexible, and the lack of interpretability reduces their credibility in clinical medical practice. This paper aims to develop a pneumonia recognition framework with interpretability, which can understand the complex relationship between lung features and related diseases in chest X-ray (CXR) images to provide high-speed analytics support for medical practice. To reduce the computational complexity to accelerate the recognition process, a novel multi-level self-attention mechanism within Transformer has been proposed to accelerate convergence and emphasize the task-related feature regions. Moreover, a practical CXR image data augmentation has been adopted to address the scarcity of medical image data problems to boost the model's performance. The effectiveness of the proposed method has been demonstrated on the classic COVID-19 recognition task using the widespread pneumonia CXR image dataset. In addition, abundant ablation experiments validate the effectiveness and necessity of all of the components of the proposed method.Comment: Accepted by the IEEE Journal of Biomedical and Health Informatic, doi: 10.1109/JBHI.2023.324794

Robust and secure zero-watermarking algorithm for medical images based on Harris-SURF-DCT and chaotic map

To protect the patient information in medical images, this article proposes a robust watermarking algorithm for medical images based on Harris-SURF-DCT. First, the corners of the medical image are extracted using the Harris corner detection algorithm, and then, the previously extracted corners are described using the method of describing feature points in the SURF algorithm to generate the feature descriptor matrix. *en, the feature descriptor matrix is processed through the perceptual hash algorithm to obtain the feature vector of the medical image, which is a binary feature vector with a size of 32 bits. Secondly, to enhance the security of the watermark information, the logistic map algorithm is used to encrypt the watermark before embedding the watermark. Finally, with the help of cryptography knowledge, third party, and zero-watermarking technology, the algorithm can embed the watermark without modifying the medical image. When extracting the watermark, the algorithm can extract the watermark from the test image without the original image. In addition, the algorithm has strong robustness to conventional attacks and geometric attacks. Especially under geometric attacks, the algorithm performs better

Robust zero watermarking algorithm for medical images based on Zernike-DCT

Digital medical system not only facilitates the storage and transmission of medical information but also brings information security problems. Aiming at the security of medical images, a robust zero watermarking algorithm for medical images based on Zernike-DCT is proposed. *e algorithm first uses a chaotic logic sequence to preprocess and encrypt the watermark, then performs edge detection and Zernike moment processing on the original medical image to get the accurate edge points, and then performs discrete cosine transform (DCT) on them to get the feature vector. Finally, it combines perceptual Hash and zero watermark technology to generate the key to complete the watermark embedding and extraction. *e algorithm has good robustness to conventional and geometric attacks, strong antinoise ability, high positioning accuracy, and processing efficiency and is superior to the classical edge detection algorithm in extraction effect. It is a stable and reliable image edge detection algorithm

A clinical prognostic model for patients with esophageal squamous cell carcinoma based on circulating tumor DNA mutation features

BackgroundFew predictive models have included circulating tumor DNA (ctDNA) indicators to predict prognosis of esophageal squamous cell carcinoma (ESCC) patients. Here, we aimed to explore whether ctDNA can be used as a predictive biomarker in nomogram models to predict the prognosis of patients with ESCC.MethodsWe included 57 patients who underwent surgery and completed a 5-year follow-up. With next-generation sequencing, a 61-gene panel was used to evaluate plasma cell-free DNA and white blood cell genomic DNA from patients with ESCC. We analyzed the relationship between the mutation features of ctDNA and the prognosis of patients with ESCC, identified candidate risk predictors by Cox analysis, and developed nomogram models to predict the 2- and 5-year disease-free survival (DFS) and overall survival (OS). The area under the curve of the receiver operating characteristic (ROC) curve, concordance index (C-index), calibration plot, and integrated discrimination improvement (IDI) were used to evaluate the performance of the nomogram model. The model was compared with the traditional tumor-nodes-metastasis (TNM) staging system.ResultsThe ROC curve showed that the average mutant allele frequency (MAF) of ctDNA variants and the number of ctDNA variants were potential biomarkers for predicting the prognosis of patients with ESCC. The predictors included in the models were common candidate predictors of ESCC, such as lymph node stage, angiolymphatic invasion, drinking history, and ctDNA characteristics. The calibration curve demonstrated consistency between the observed and predicted results. Moreover, our nomogram models showed clear prognostic superiority over the traditional TNM staging system (based on C-index, 2-year DFS: 0.82 vs. 0.64; 5-year DFS: 0.78 vs. 0.65; 2-year OS: 0.80 vs. 0.66; 5-year OS: 0.77 vs. 0.66; based on IDI, 2-year DFS: 0.33, p <0.001; 5-year DFS: 0.18, p = 0.04; 2-year OS: 0.28, p <0.001; 5-year OS: 0.15, p = 0.04). The comprehensive scores of the nomogram models could be used to stratify patients with ESCC.ConclusionsThe novel nomogram incorporating ctDNA features may help predict the prognosis of patients with resectable ESCC. This model can potentially be used to guide the postoperative management of ESCC patients in the future, such as adjuvant therapy and follow-up

A Unified Approach to Nested and Non-Nested Slots for Spoken Language Understanding

As chatbots become more popular, multi-intent spoken language understanding (SLU) has received unprecedented attention. Multi-intent SLU, which primarily comprises the two subtasks of multiple intent detection (ID) and slot filling (SF), has the potential for widespread implementation. The two primary issues with the current approaches are as follows: (1) They cannot solve the problem of slot nesting; (2) The performance and inference rate of the model are not high enough. To address these issues, we suggest a multi-intent joint model based on global pointers to handle nested and non-nested slots. Firstly, we constructed a multi-dimensional type-slot label interaction network (MTLN) for subsequent intent decoding to enhance the implicit correlation between intents and slots, which allows for more adequate information about each other. Secondly, the global pointer network (GP) was introduced, which not only deals with nested and non-nested slots and slot incoherence but also has a faster inference rate and better performance than the baseline model. On two multi-intent datasets, the proposed model achieves state-of-the-art results on MixATIS with 1.6% improvement of intent Acc, 0.1% improvement of slot F1 values, 3.1% improvement of sentence Acc values, and 1.2%, 1.1% and 4.5% performance improvements on MixSNIPS, respectively. Meanwhile, the inference rate is also improved

PD-1 blockade immunotherapy as a successful rescue treatment for disseminated adenovirus infection after allogeneic hematopoietic stem cell transplantation

Abstract Disseminated adenovirus infection is a complication with a relatively high mortality rate among patients undergoing hematopoietic stem cell transplantation. The low efficacy and poor availability of current treatment options are of major concern. Programmed cell death 1 (PD-1) blockade has been used to treat several chronic viral infections. Herein, we report a case of disseminated adenovirus infection in the early posttransplant period. The patient was diagnosed with diffuse large B-cell lymphoma at first and underwent 8 cycles of chemotherapy, including rituximab. She was subsequently diagnosed with acute myeloid leukemia and received haploidentical transplantation. She was diagnosed with Epstein‒Barr virus (EBV)-positive posttransplant lymphoproliferative disorder (PTLD) 2 months after the transplant, and 3 doses of rituximab were administered. The patient was diagnosed with disseminated adenovirus infection with upper respiratory tract, gastrointestinal tract and blood involved at 3 months after transplantation. She was first treated with a reduction in immunosuppression, cidofovir and ribavirin. Then, the patient received salvage treatment with the PD-1 inhibitor sintilimab (200 mg) after achieving no response to conventional therapy. The adenovirus was cleared 3 weeks later, and concomitant EBV was also cleared. Although the patient developed graft-versus-host disease of the liver after the administration of the PD-1 inhibitor, she was cured with steroid-free therapy. Therefore, PD-1 blockade immunotherapy can be considered a promising treatment option for patients with disseminated adenovirus infection after transplantation, with fully weighing the hazards of infection and the side effects of this therapy

Quality‐of‐life, mental health, and perspective on TKI dose reduction as a prelude to discontinuation in chronic phase chronic myeloid leukemia

Abstract Background Treatment‐free remission (TFR) has become the main target for chronic myeloid leukemia (CML). Tyrosine kinase inhibitors (TKI) dose optimization is crucial in managing adverse events, and improving adherence in clinical practice. In persons achieving a deep molecular response (DMR), some data suggest TKI dose reduction before discontinuation does not change success rate of achieving TFR, but this is controversial. However, data on quality‐of‐life (QoL) and mental health in CML patients with full‐dose TKI, low‐dose TKI, and TKI discontinuation are limited. Moreover, recent evidence indicating the feasibility of TKI dose reduction and discontinuation after dose reduction, which may change CML patients' perspectives on TKI discontinuation. Methods We conducted a cross‐sectional study using online questionnaires to explore the QoL, mental health in patients with diverse TKI dose, and perspective on TKI dose reduction as a prelude to discontinuation. Results 1450 responses were included in the analysis. 44.3% of respondents reported a moderate‐to‐severe impact of TKI treatment on their QoL. 17% of respondents had moderate‐to‐severe anxiety. 24.4% of respondents had moderate‐to‐severe depression. In 1326 patients who had not discontinued their medication, 1055 (79.6%) patients reported they would try TKI discontinuation because of concerns over side effects of long‐term medication (67.9%), financial burden (68.7%), poor QoL (77.9%), pregnancy needs (11.6%), anxiety and depression while taking TKI (20.8%), inconvenience of TKI treatment (22.2%). 613 of 817 (75.0%) patients on full‐dose TKI therapy indicated they preferred trying a dose reduction before discontinuing TKI therapy after dose reduction compared with 31 (3.8%) preferring no dose reduction before stopping. Conclusions TKI dose reduction showed a significant improvement of patients' QoL and mental health, comparable to the effect of TKI discontinuation. Most patients indicated they preferred dose reduction before stopping TKI therapy. In clinical practice, TKI dose reduction can be considered as a bridge from full‐dose treatment to discontinuation. Our results showed that tyrosine kinase inhibitors (TKI) dose reduction showed a significant improvement of patients' quality‐of‐life and mental health, comparable to the effect of TKI discontinuation. Most patients desire to discontinue TKI in the future. TKI discontinuation after dose reduction is more acceptable compared to discontinuing it directly. In clinical practice, TKI dose reduction can be considered as a bridge from full‐dose treatment to discontinuation. Please do not hesitate to contact me in case further clarification is needed with this submission