A Learnable Physical Deep Learning Model for Sparse Limited View Photoacoustic Tomography.

The photoacoustic tomography system requires custom-built and extensive array elements to achieve high-sensitivity and high-contrast imaging in clinical practice. However, the manufacture of photoacoustic transducers with extensive elements is costly and limits their flexible clinical applications. In this work, we focused on the research of sparse reconstruction algorithms to alleviate the demand for manufacturing.

Null subtraction imaging combined with modified delay multiply-and-sum beamforming for coherent plane-wave compounding.

Coherent plane-wave compounding, while efficient for ultrafast ultrasound imaging, yields lower image quality due to unfocused waves. Delay multiply-and-sum (DMAS) beamformer is one of the representative coherence-based methods which can improve images quality, but suffers from poor speckle quality brought by oversuppression. Current DMAS-based methods involve trade-offs between contrast, resolution, and speckle preservation.

A transformer-guided cross-modality adaptive feature fusion framework for esophageal gross tumor volume segmentation.

Background And Objective: Accurate segmentation of esophageal gross tumor volume (GTV) indirectly enhances the efficacy of radiotherapy for patients with esophagus cancer. In this domain, learning-based methods have been employed to fuse cross-modality positron emission tomography (PET) and computed tomography (CT) images, aiming to improve segmentation accuracy. This fusion is essential as it combines functional metabolic information from PET with anatomical information from CT, providing complementary information.

Condition control training-based ConVMLP-ResU-Net for semantic segmentation of esophageal cancer in F-FDG PET/CT images.

The precise delineation of esophageal gross tumor volume (GTV) on medical images can promote the radiotherapy effect of esophagus cancer. This work is intended to explore effective learning-based methods to tackle the challenging auto-segmentation problem of esophageal GTV. By employing the progressive hierarchical reasoning mechanism (PHRM), we devised a simple yet effective two-stage deep framework, ConVMLP-ResU-Net.

Automatic segmentation of esophageal gross tumor volume in F-FDG PET/CT images via GloD-LoATUNet.

Background And Objective: For esophageal squamous cell carcinoma, radiotherapy is one of the primary treatments. During the planning before radiotherapy, the intractable task is to precisely delineate the esophageal gross tumor volume (GTV) on medical images. In current clinical practice, the manual delineation suffers from high intra- and inter-rater variability, while also exhausting the oncologists on a treadmill.

CM-SegNet: A deep learning-based automatic segmentation approach for medical images by combining convolution and multilayer perceptron.

Accurate segmentation of lesions in medical images is of great significance for clinical diagnosis and evaluation. The low contrast between lesions and surrounding tissues increases the difficulty of automatic segmentation, while the efficiency of manual segmentation is low. In order to increase the generalization performance of segmentation model, we proposed a deep learning-based automatic segmentation model called CM-SegNet for segmenting medical images of different modalities.

Feasibility of a Generative Adversarial Network for Artifact Removal in Experimental Photoacoustic Imaging.

Photoacoustic tomography (PAT) reconstruction is an expeditiously growing interest among biomedical researchers because of its possible transition from laboratory to clinical pre-eminence. Nonetheless, the PAT inverse problem is yet to achieve an optimal solution in rapid and precise reconstruction under practical constraints. Precisely, the sparse sampling problem and random noise are the main impediments to attaining accuracy but in support of rapid PAT reconstruction.

Gross Tumor Volume Definition and Comparative Assessment for Esophageal Squamous Cell Carcinoma From 3D F-FDG PET/CT by Deep Learning-Based Method.

Background: The accurate definition of gross tumor volume (GTV) of esophageal squamous cell carcinoma (ESCC) can promote precise irradiation field determination, and further achieve the radiotherapy curative effect. This retrospective study is intended to assess the applicability of leveraging deep learning-based method to automatically define the GTV from 3D F-FDG PET/CT images of patients diagnosed with ESCC.

Methods: We perform experiments on a clinical cohort with 164 F-FDG PET/CT scans.

G-DipC: An Improved Feature Representation Method for Short Sequences to Predict the Type of Cargo in Cell-Penetrating Peptides.

Cell-penetrating peptides (CPPs) are functional short peptides with high carrying capacity. CPP sequences with targeting functions for the highly efficient delivery of drugs to target cells. In this paper, which is focused on the prediction of the cargo category of CPPs, a biocomputational model is constructed to efficiently distinguish the category of cargo carried by CPPs as macromolecular carriers among the seven known deliverable cargo categories.

Protein subnuclear localization based on a new effective representation and intelligent kernel linear discriminant analysis by dichotomous greedy genetic algorithm.

A wide variety of methods have been proposed in protein subnuclear localization to improve the prediction accuracy. However, one important trend of these means is to treat fusion representation by fusing multiple feature representations, of which, the fusion process takes a lot of time. In view of this, this paper novelly proposed a method by combining a new single feature representation and a new algorithm to obtain good recognition rate.