BSDA: Bayesian Random Semantic Data Augmentation for Medical Image Classification.

Data augmentation is a crucial regularization technique for deep neural networks, particularly in medical imaging tasks with limited data. Deep learning models are highly effective at linearizing features, enabling the alteration of feature semantics through the shifting of latent space representations-an approach known as semantic data augmentation (SDA). The paradigm of SDA involves shifting features in a specified direction.

A BRD4-Targeting Photothermal Agent for Controlled Protein Degradation.

BRD4 protein plays a pivotal role in cell cycle regulation and differentiation. Disrupting the activity of BRD4 has emerged as a promising strategy for inhibiting the growth and proliferation of cancer cells. Herein, we introduced a BRD4-targeting photothermal agent for controlled protein degradation, aiming to enhance low-temperature photothermal therapy (PTT) for cancer treatment.

A Golgi Apparatus-Targeted Photothermal Agent with Protein Anchoring for Enhanced Cancer Photothermal Therapy.

The Golgi apparatus (GA) is central in shuttling proteins from the endoplasmic reticulum to different cellular areas. Therefore, targeting the GA to precisely destroy its proteins through local heat could induce apoptosis, offering a potential avenue for effective cancer therapy. Herein, a GA-targeted photothermal agent based on protein anchoring is introduced for enhanced photothermal therapy of tumor through the modification of near-infrared molecular dye with maleimide derivative and benzene sulfonamide.

[A multimodal medical image contrastive learning algorithm with domain adaptive denormalization].

Recently, deep learning has achieved impressive results in medical image tasks. However, this method usually requires large-scale annotated data, and medical images are expensive to annotate, so it is a challenge to learn efficiently from the limited annotated data. Currently, the two commonly used methods are transfer learning and self-supervised learning.

[A meta-learning based method for segmentation of few-shot magnetic resonance images].

When applying deep learning algorithms to magnetic resonance (MR) image segmentation, a large number of annotated images are required as data support. However, the specificity of MR images makes it difficult and costly to acquire large amounts of annotated image data. To reduce the dependence of MR image segmentation on a large amount of annotated data, this paper proposes a meta-learning U-shaped network (Meta-UNet) for few-shot MR image segmentation.

[Hepatocellular carcinoma segmentation and pathological differentiation degree prediction method based on multi-task learning].

Hepatocellular carcinoma (HCC) is the most common liver malignancy, where HCC segmentation and prediction of the degree of pathological differentiation are two important tasks in surgical treatment and prognosis evaluation. Existing methods usually solve these two problems independently without considering the correlation of the two tasks. In this paper, we propose a multi-task learning model that aims to accomplish the segmentation task and classification task simultaneously.

Feasibility of intelligent drug control in the maintenance phase of general anesthesia based on convolutional neural network.

Background: The growth and aging process of the human population has accelerated the increase in surgical procedures. Yet, the demand for increasing operations can be hardly met since the training of anesthesiologists is usually a long-term process. Closed-loop artificial intelligence (AI) model provides the possibility to solve intelligent decision-making for anesthesia auxiliary control and, as such, has allowed breakthroughs in closed-loop control of clinical practices in intensive care units (ICUs).

Prediction and Evaluation of Machine Learning Algorithm for Prediction of Blood Transfusion during Cesarean Section and Analysis of Risk Factors of Hypothermia during Anesthesia Recovery.

Objective: To explore the application of machine learning algorithm in the prediction and evaluation of cesarean section, predicting the amount of blood transfusion during cesarean section and to analyze the risk factors of hypothermia during anesthesia recovery.

Methods: (1)Through the hospital electronic medical record of medical system, a total of 600 parturients who underwent cesarean section in our hospital from June 2019 to December 2020 were included. The maternal age, admission time, diagnosis, and other case data were recorded.

A Protein-Binding Molecular Photothermal Agent for Tumor Ablation.

Photothermal therapy usually requires a high power density to activate photothermal agent for effective treatment, which inevitably leads to damage to normal tissues and inflammation in tumor tissues. Herein, we rationally design a protein-binding strategy to build a molecular photothermal agent for photothermal ablation of tumor. The synthesized photothermal agent can covalently bind to the thiol groups on the intracellular proteins.

Interactive Echocardiography Translation Using Few-Shot GAN Transfer Learning.

Background: Interactive echocardiography translation is an efficient educational function to master cardiac anatomy. It strengthens the student's understanding by pixel-level translation between echocardiography and theoretically sketch images. Previous research studies split it into two aspects of image segmentation and synthesis.

[Medical computer-aided detection method based on deep learning].

This paper performs a comprehensive study on the computer-aided detection for the medical diagnosis with deep learning. Based on the region convolution neural network and the prior knowledge of target, this algorithm uses the region proposal network, the region of interest pooling strategy, introduces the multi-task loss function: classification loss, bounding box localization loss and object rotation loss, and optimizes it by end-to-end. For medical image it locates the target automatically, and provides the localization result for the next stage task of segmentation.

[Left ventricle segmentation in echocardiography based on adaptive mean shift].

The use of echocardiography ventricle segmentation can obtain ventricular volume parameters, and it is helpful to evaluate cardiac function. However, the ultrasound images have the characteristics of high noise and difficulty in segmentation, bringing huge workload to segment the object region manually. Meanwhile, the automatic segmentation technology cannot guarantee the segmentation accuracy.

[Research on failure reasons of surgical treatment of obstructive sleep apnea hypopnea syndrome and reoperation method].

Objective: To analyze failure reasons of surgical treatment of obstructive sleep apnea hypopnea syndrome (OSAHS), and explore the methods of reoperation.

Method: By selecting 27 patients, who accepted surgical treatment for OSAHS and recurred, we analyzed failure reasons and obstructive location by apneagraph, nasopharyngeal 3D-CT, electronic nasopharynlaryngoscope. Among them, 14 patients accepted reoperation, such as uvulopalatopharyngoplasty (UPPP), nasoendoscopic surgery, adenoidectomy, partial glossectomy, tracheotomy were applied matching to differential obstructive location.

[The clinical investigation of the potential complications of H-UPPP surgery in removing the partial pharyngeal muscle].

Objective: In order to improve the postoperative effect of modified UPPP, removing the partial pharyngeal muscle in surgery, we investigate the postoperative effect, the characteristics of pharyngeal cavity and the potential complications in OSAHS patients.

Method: To choose 82 OSAHS patients with obstructive oropharyngeal plane diagnosed by Apneagraphy (AG), Fibre nasopharyngoscope combined with Müller examination and nasopharyngeal 3D-CT, which had completed clinical data inpatients in the anesthesia underwent of the partial pharyngeal muscles in the postoperative, divided into a control group of 26 cases, operating the H-UPPP surgery which did not remove partial pharyngeal muscle; The experimental group of 56 cases did a H-UPPP surgical which removed partial pharyngeal muscle of possible concurrent symptoms such as nasal regurgitation, Eustachian tube dysfunction and other follow-up study in six months after the monthly telephone follow-up or outpatient exams to understand the disease. Patients were evaluated the sleepiness by ESS(Epworth sleepiness scale) in 6 months after the surgery, compared with the preoperative ESS scores, do a t test for statistical analysis.

[Significance of AG in OSAHS operation treatment guidance and evaluation of postoperative efficacy].

Objective: Apneagraph can be used to discuss which the best operation scheme is for OSAHS. Effects of uvulopalatopharyngoplasty can be assessed by Apneagraph in obstructive sleep apnea hypopnea syndrome (OSAHS) patients.

Method: Fifty-six patients with OSAHS received the modified UPPP operation were randomly selected in our hospital.