Three-dimensional semi-supervised lumbar vertebrae region of interest segmentation based on MAE pre-training.

Background:: The annotation of the regions of interest (ROI) of lumbar vertebrae by radiologists for bone density assessment is a tedious and time-intensive task. However, deep learning (DL) methods for image segmentation has the potential to substitute manual annotations which can significantly improve the efficiency of clinical diagnostics.

Objective:: The paper proposes a semi-supervised three-dimensional (3D) segmentation method for the ROI of lumbar vertebrae by integrating the tube masking masked autoencoder (MAE) pre-training.

Policy Learning for Actively Labeled Sample Selection on Lumbar Semi-supervised Classification.

Large labeled data bring significant performance improvement, but acquiring labeled medical data is particularly challenging due to the laborious, time-consuming, and medically qualified annotation. Semi-supervised learning has been employed to leverage unlabeled data. However, the quality and quantity of annotated data have a great influence on the performance of the semi-supervised model.

Semantic contrast with uncertainty-aware pseudo label for lumbar semi-supervised classification.

Background: Lumbar disc herniation (LDH) is a prevalent spinal disease that can result in severe pain, with Magnetic resonance imaging (MRI) serving as a commonly diagnostic tool. However, annotating numerous MRI images, necessary for deep learning based LDH diagnosis, can be challenging and labor-intensive. Semi-supervised learning, mainly utilizing pseudo labeling and consistency regularization, can leverage limited labeled images and abundant unlabeled images.

Deep learning-based detection and classification of lumbar disc herniation on magnetic resonance images.

Background: The severity assessment of lumbar disc herniation (LDH) on MR images is crucial for selecting suitable surgical candidates. However, the interpretation of MR images is time-consuming and requires repetitive work. This study aims to develop and evaluate a deep learning-based diagnostic model for automated LDH detection and classification on lumbar axial T2-weighted MR images.

Multi-view features integrated 2D\3D Net for glomerulopathy histologic types classification using ultrasound images.

Background And Objective: Early diagnoses and rational therapeutics of glomerulopathy can control progression and improve prognosis. The gold standard for the diagnosis of glomerulopathy is pathology by renal biopsy, which is invasive and has many contraindications. We aim to use renal ultrasonography for histologic classification of glomerulopathy.

Preliminary study on the application of renal ultrasonography radiomics in the classification of glomerulopathy.

Background: The aim of this study was to investigate the potential use of renal ultrasonography radiomics features in the histologic classification of glomerulopathy.

Methods: A total of 623 renal ultrasound images from 46 membranous nephropathy (MN) and 22 IgA nephropathy patients were collected. The cases and images were divided into a training group (51 cases with 470 images) and a test group (17 cases with 153 images).

Multicenter Computer-Aided Diagnosis for Lymph Nodes Using Unsupervised Domain-Adaptation Networks Based on Cross-Domain Confounding Representations.

To achieve the robust high-performance computer-aided diagnosis systems for lymph nodes, CT images may be typically collected from multicenter data, which cause the isolated performance of the model based on different data source centers. The variability adaptation problem of lymph node data which is related to the problem of domain adaptation in deep learning differs from the general domain adaptation problem because of the typically larger CT image size and more complex data distributions. Therefore, domain adaptation for this problem needs to consider the shared feature representation and even the conditioning information of each domain so that the adaptation network can capture significant discriminative representations in a domain-invariant space.

Application of CT radiomics in prediction of early recurrence in hepatocellular carcinoma.

Purpose: To appraise the ability of the computed tomography (CT) radiomics signature for prediction of early recurrence (ER) in patients with hepatocellular carcinoma (HCC).

Methods: A set of 325 HCC patients were enrolled in this retrospective study and the whole dataset was divided into 2 cohorts, including "training set" (225 patients) and "test set" (100 patients). All patients who underwent partial hepatectomy were followed up at least within 1 year.

[Quantitative analysis of hepatocellular carcinomas pathological grading in non-contrast magnetic resonance images].

In order to solve the pathological grading of hepatocellular carcinomas (HCC) which depends on biopsy or surgical pathology invasively, a quantitative analysis method based on radiomics signature was proposed for pathological grading of HCC in non-contrast magnetic resonance imaging (MRI) images. The MRI images were integrated to predict clinical outcomes using 328 radiomics features, quantifying tumour image intensity, shape and text, which are extracted from lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) were used to select the most-predictive radiomics features for the pathological grading.

Fully Convolutional DenseNet with Multiscale Context for Automated Breast Tumor Segmentation.

Breast tumor segmentation plays a crucial role in subsequent disease diagnosis, and most algorithms need interactive prior to firstly locate tumors and perform segmentation based on tumor-centric candidates. In this paper, we propose a fully convolutional network to achieve automatic segmentation of breast tumor in an end-to-end manner. Considering the diversity of shape and size for malignant tumors in the digital mammograms, we introduce multiscale image information into the fully convolutional dense network architecture to improve the segmentation precision.

Multi-level features combined end-to-end learning for automated pathological grading of breast cancer on digital mammograms.

We propose to discriminate the pathological grades directly on digital mammograms instead of pathological images. An end-to-end learning algorithm based on the combined multi-level features is proposed. Low-level features are extracted and selected by supervised LASSO logistic regression.

Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature.

Purpose: This study was conducted in order to investigate the value of magnetic resonance imaging (MRI)-based radiomics signatures for the preoperative prediction of hepatocellular carcinoma (HCC) grade.

Methods: Data from 170 patients confirmed to have HCC by surgical pathology were divided into a training group (n = 125) and a test group (n = 45). The radiomics features of tumours based on both T1-weighted imaging (WI) and T2WI were extracted by using Matrix Laboratory (MATLAB), and radiomics signatures were generated using the least absolute shrinkage and selection operator (LASSO) logistic regression model.