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Automated analysis of the vessel structure in intravascular optical coherence tomography (IVOCT) images is critical to assess the health status of vessels and monitor coronary artery disease progression. However, deep learning‐based methods usually require well‐annotated large datasets, which are difficult to obtain in the field of medical image analysis. Hence, an automatic layers segmentation method based on meta‐learning was proposed, which can simultaneously extract the surfaces of the lumen, intima, media, and adventitia using a handful of annotated samples. Specifically, we leverage a bi‐level gradient strategy to train a meta‐learner for capturing the shared meta‐knowledge among different anatomical layers and quickly adapting to unknown anatomical layers. Then, a Claw‐type network and a contrast consistency loss were designed to better learn the meta‐knowledge according to the characteristic of annotation of the lumen and anatomical layers. Experimental results on the two cardiovascular IVOCT datasets show that the proposed method achieved state‐of‐art performance. [ABSTRACT FROM AUTHOR]

Automatic detection of thin‐cap fibroatheroma (TCFA) on intravascular optical coherence tomography images is essential for the prevention of acute coronary syndrome. However, existing methods need to mark the exact location of TCFAs on each frame as supervision, which is extremely time‐consuming and expensive. Hence, a new weakly supervised framework is proposed to detect TCFAs using only image‐level tags as supervision. The framework comprises cut, feature extraction, relation, and detection modules. First, based on prior knowledge, a cut module was designed to generate a small number of specific region proposals. Then, to learn global information, a relation module was designed to learn the spatial adjacency and order relationships at the feature level, and an attention‐based strategy was introduced in the detection module to effectively aggregate the classification results of region proposals as the image‐level predicted score. The results demonstrate that the proposed method surpassed the state‐of‐the‐art weakly supervised detection methods. [ABSTRACT FROM AUTHOR]

BCL-2-associated X protein (BAX) is a promising therapeutic target for activating or restraining apoptosis in diseases of pathologic cell survival or cell death, respectively. In response to cellular stress, BAX transforms from a quiescent cytosolic monomer into a toxic oligomer that permeabilizes the mitochondria, releasing key apoptogenic factors. The mitochondrial lipid trans-2-hexadecenal (t-2-hex) sensitizes BAX activation by covalent derivatization of cysteine 126 (C126). In this study, we performed a disulfide tethering screen to discover C126-reactive molecules that modulate BAX activity. We identified covalent BAX inhibitor 1 (CBI1) as a compound that selectively derivatizes BAX at C126 and inhibits BAX activation by triggering ligands or point mutagenesis. Biochemical and structural analyses revealed that CBI1 can inhibit BAX by a dual mechanism of action: conformational constraint and competitive blockade of lipidation. These data inform a pharmacologic strategy for suppressing apoptosis in diseases of unwanted cell death by covalent targeting of BAX C126., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Lymphomas are a group of malignant tumors developed from lymphocytes, which may occur in many organs. Therefore, accurately distinguishing lymphoma from solid tumors is of great clinical significance. Due to the strong ability of graph structure to capture the topology of the micro-environment of cells, graph convolutional networks (GCNs) have been widely used in pathological image processing. Nevertheless, the softmax classification layer of the graph convolutional models cannot drive learned representations compact enough to distinguish some types of lymphomas and solid tumors with strong morphological analogies on H&E-stained images. To alleviate this problem, a prototype learning based model is proposed, namely graph convolutional prototype network (GCPNet). Specifically, the method follows the patch-to-slide architecture first to perform patch-level classification and obtain image-level results by fusing patch-level predictions. The classification model is assembled with a graph convolutional feature extractor and prototype-based classification layer to build more robust feature representations for classification. For model training, a dynamic prototype loss is proposed to give the model different optimization priorities at different stages of training. Besides, a prototype reassignment operation is designed to prevent the model from getting stuck in local minima during optimization. Experiments are conducted on a dataset of 183 Whole slide images (WSI) of gastric mucosa biopsy. The proposed method achieved superior performance than existing methods.Clinical relevance- The work proposed a new deep learning framework tailored to lymphoma recognition on pathological image of gastric mucosal biopsy to differentiate lymphoma, adenocarcinoma and inflammation.

Automatic detection of thin-cap fibroatheroma (TCFA) is essential to prevent acute coronary syndrome. Hence, in this paper, a method is proposed to detect TCFAs by directly classifying each A-line using multi-view intravascular optical coherence tomography (IVOCT) images. To solve the problem of false positives, a multi-input-output network was developed to implement image-level classification and A-line-based classification at the same time, and a contrastive consistency term was designed to ensure consistency between two tasks. In addition, to learn spatial and global information and obtain the complete extent of TCFAs, an architecture and a regional connectivity constraint term are proposed to classify each A-line of IVOCT images. Experimental results obtained on the 2017 China Computer Vision Conference IVOCT dataset show that the proposed method achieved state-of-art performance with a total score of 88.7±0.88 % , overlap rate of 88.64±0.26 % , precision rate of 84.34±0.86 % , and recall rate of 93.67±2.29 % .

Pseudo-label-based unsupervised domain adaptation (UDA) has increasingly gained interest in medical image analysis, aiming to solve the problem of performance degradation of deep neural networks when dealing with unseen data. Although it has achieved great success, it still faced two significant challenges: improving pseudo labels' precision and mitigating the effects caused by noisy pseudo labels. To solve these problems, we propose a novel UDA framework based on label distribution learning, where the problem is formulated as noise label correcting and can be solved by converting a fixed categorical value (pseudo labels on target data) to a distribution and iteratively update both network parameters and label distribution to correct noisy pseudo labels, and then these labels are used to re-train the model. We have extensively evaluated our framework with vulnerable plaques detection between two IVOCT datasets. Experimental results show that our UDA framework is effective in improving the detection performance of unlabeled target images.

Previous studies have proved that the vulnerable plaque is a major factor leading to the onset of acute coronary syndrome (ACS). Recognizing vulnerable plaques is essential for cardiologists to treat illnesses, early. However, this task often comes with the challenge of insufficient annotated data sets and subtle differences between lesion regions and normal regions. In this paper, we apply the visual attention model with deep neural network to improve the performance of recognizing vulnerable plaques. There are two key ideas about our method: 1) using a top-down attention model to extract salient regions (blood vessels) according to the doctor's prior knowledge, and 2) employing a multi-task neural network to complete the recognition task. The first branch, a typical classification task, is to distinguish whether the image contains vulnerable plaques. The other branch uses a column-wise segmentation to locate vulnerable plaques. We have verified the effectiveness of our proposed method on the data set provided by 2017 CCCV-IVOCT Challenge. The proposed method obtains good performance.