Research on prediction of human oral bioavailability of drugs based on improved deep forest.

Human oral bioavailability is a crucial factor in drug discovery. In recent years, researchers have constructed a variety of different prediction models. However, given the limited size of human oral bioavailability data sets, the challenge of making accurate predictions with small sample sizes has become a critical issue in the field.

AFANet: Adaptive feature aggregation for polyp segmentation.

In terms of speed and accuracy, the deep learning-based polyp segmentation method is superior. It is essential for the early detection and treatment of colorectal cancer and has the potential to greatly reduce the disease's overall prevalence. Due to the various forms and sizes of polyps, as well as the blurring of the boundaries between the polyp region and the surrounding mucus, most existing algorithms are unable to provide highly accurate colorectal polyp segmentation.

FECFusion: Infrared and visible image fusion network based on fast edge convolution.

The purpose of infrared and visible image fusion is to integrate the complementary information from heterogeneous images in order to enhance their detailed scene information. However, existing deep learning fusion methods suffer from an imbalance between fusion performance and computational resource consumption. Additionally, fusion layers or fusion rules fail to effectively combine heteromodal feature information.

MSF-Net: A Lightweight Multi-Scale Feature Fusion Network for Skin Lesion Segmentation.

Segmentation of skin lesion images facilitates the early diagnosis of melanoma. However, this remains a challenging task due to the diversity of target scales, irregular segmentation shapes, low contrast, and blurred boundaries of dermatological graphics. This paper proposes a multi-scale feature fusion network (MSF-Net) based on comprehensive attention convolutional neural network (CA-Net).

Towards Chinese clinical named entity recognition by dynamic embedding using domain-specific knowledge.

The task of electronic medical record named entity recognition (NER) refers to automatically identify all kinds of named entities in the medical record text. Chinese clinical NER remains a major challenge. One of the main reasons is that Chinese word segmentation will lead to the wrong downstream works.

Artifacts detection-based adaptive filtering to noise reduction of strain imaging.

Strain imaging in medical ultrasound is the imaging modality of elastic properties of biological tissue. In general, strain image will suffer from artifacts noise, which degrades lesion detectability and increases the likelihood of misdiagnosis. How to both suppress artifacts effectively and preserve the structure is vital for diagnosis and also for image post-processing.

Ultrasound speckle reduction based on fractional order differentiation.

Purpose: Ultrasound images show a granular pattern of noise known as speckle that diminishes their quality and results in difficulties in diagnosis. To preserve edges and features, this paper proposes a fractional differentiation-based image operator to reduce speckle in ultrasound.

Methods: An image de-noising model based on fractional partial differential equations with balance relation between k (gradient modulus threshold that controls the conduction) and v (the order of fractional differentiation) was constructed by the effective combination of fractional calculus theory and a partial differential equation, and the numerical algorithm of it was achieved using a fractional differential mask operator.

A fast bilateral filter with application to artefact reduction.

Elastography in medical ultrasound is an imaging technique that displays information about tissue stiffness. However, elastography suffers from artefact noise that may come from two dominant sources: decorrelation error and amplitude modulation error. In order to reduce artefact and improve the quality of ultrasonic elastography, a fast bilateral filter is proposed in this study based on local histogram.