UM-Net: Rethinking ICGNet for polyp segmentation with uncertainty modeling.

Automatic segmentation of polyps from colonoscopy images plays a critical role in the early diagnosis and treatment of colorectal cancer. Nevertheless, some bottlenecks still exist. In our previous work, we mainly focused on polyps with intra-class inconsistency and low contrast, using ICGNet to solve them.

A Double-Teacher Model Capable of Exploiting Isomorphic and Heterogeneous Discrepancy Information for Medical Image Segmentation.

Deep learning, with continuous development, has achieved relatively good results in the field of left atrial segmentation, and numerous semi-supervised methods in this field have been implemented based on consistency regularization to obtain high-performance 3D models by training. However, most semi-supervised methods focus on inter-model consistency and ignore inter-model discrepancy. Therefore, we designed an improved double-teacher framework with discrepancy information.

ASCRB: Multi-view based attentional feature selection for CircRNA-binding site prediction.

CircRNA is a non-coding RNA with a special circular structure, which plays a key role in a variety of life activities by interacting with RNA-binding proteins through CircRNA binding sites. Therefore, accurately identifying CircRNA binding sites is of great importance for gene regulation. In previous studies, most of the methods are based on single-view or multi-view features.

TSRL-Net: Target-aware supervision residual learning for stroke segmentation.

Accurate stroke segmentation is a crucial task in establishing a computer-aided diagnostic system for brain diseases. However, reducing false negatives and accurately segmenting strokes in MRI images is often challenging because of the class imbalance and intraclass ambiguities problems. To address these issues, we propose a novel target-aware supervision residual learning framework for stroke segmentation.

Multimodal adversarial representation learning for breast cancer prognosis prediction.

With the increasing incidence of breast cancer, accurate prognosis prediction of breast cancer patients is a key issue in current cancer research, and it is also of great significance for patients' psychological rehabilitation and assisting clinical decision-making. Many studies that integrate data from different heterogeneous modalities such as gene expression profile, clinical data, and copy number alteration, have achieved greater success than those with only one modality in prognostic prediction. However, many of these approaches that exist fail to dramatically reduce the modality gap by aligning multimodal distributions.

JLCRB: A unified multi-view-based joint representation learning for CircRNA binding sites prediction.

CircRNAs usually bind to the corresponding RBPs(RNA Binding proteins) and play a key role in gene regulation. Therefore, it is important to identify the binding sites of RBPs on CircRNAs for the regulation of certain diseases. Due to the information provided by the single view feature is limited, the current mainstream methods are mainly to detect the RBP binding sites by constructing multi-view models.

AGMR-Net: Attention-guided multiscale recovery framework for stroke segmentation.

Automatic and accurate lesion segmentation is critical to the clinical estimation of the lesion status of stroke diseases and appropriate diagnostic systems. Although existing methods have achieved remarkable results, their further adoption is hindered by: (1) intraclass inconsistency, i.e.

DeepBtoD: Improved RNA-binding proteins prediction via integrated deep learning.

RNA-binding proteins (RBPs) have crucial roles in various cellular processes such as alternative splicing and gene regulation. Therefore, the analysis and identification of RBPs is an essential issue. However, although many computational methods have been developed for predicting RBPs, a few studies simultaneously consider local and global information from the perspective of the RNA sequence.

Two-stage segmentation network with feature aggregation and multi-level attention mechanism for multi-modality heart images.

Accurate segmentation of cardiac substructures in multi-modality heart images is an important prerequisite for the diagnosis and treatment of cardiovascular diseases. However, the segmentation of cardiac images remains a challenging task due to (1) the interference of multiple targets, (2) the imbalance of sample size. Therefore, in this paper, we propose a novel two-stage segmentation network with feature aggregation and multi-level attention mechanism (TSFM-Net) to comprehensively solve these challenges.

Deep Multi-Label Joint Learning for RNA and DNA-Binding Proteins Prediction.

The recognition of DNA- (DBPs) and RNA-binding proteins (RBPs) is not only conducive to understanding cell function, but also a challenging task. Previous studies have shown that these proteins are usually considered separately due to different binding domains. In addition, due to the high similarity between DBPs and RBPs, it is possible for DBPs predictor to predict RBPs as DBPs, and vice versa, which leads to high cross-prediction rate.

DFpin: Deep learning-based protein-binding site prediction with feature-based non-redundancy from RNA level.

The interaction between proteins and RNA is closely related to various human diseases. Computer-aided drug design can be facilitated by detecting the RNA sites that bind proteins. However, due to the aggregation of binding sites in RNA sequences, high sample similarity occurs when extracting RNA fragments by using a sliding window.

Constraint-Based Unsupervised Domain Adaptation Network for Multi-Modality Cardiac Image Segmentation.

The cardiac CT and MRI images depict the various structures of the heart, which are very valuable for analyzing heart function. However, due to the difference in the shape of the cardiac images and imaging techniques, automatic segmentation is challenging. To solve this challenge, in this paper, we propose a new constraint-based unsupervised domain adaptation network.

TSU-net: Two-stage multi-scale cascade and multi-field fusion U-net for right ventricular segmentation.

Accurate segmentation of the right ventricle from cardiac magnetic resonance images (MRI) is a critical step in cardiac function analysis and disease diagnosis. It is still an open problem due to some difficulties, such as a large variety of object sizes and ill-defined borders. In this paper, we present a TSU-net network that grips deeper features and captures targets of different sizes with multi-scale cascade and multi-field fusion in the right ventricle.

Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning.

DNA-binding proteins (DBPs) play pivotal roles in many biological functions such as alternative splicing, RNA editing, and methylation. Many traditional machine learning (ML) methods and deep learning (DL) methods have been proposed to predict DBPs. However, these methods either rely on manual feature extraction or fail to capture long-term dependencies in the DNA sequence.

Using Chou's 5-Step Rule to Predict DNA-Protein Binding with Multi-scale Complementary Feature.

It is well known that DNA-protein binding (DPB) prediction is not only beneficial to understand the regulation mechanism of gene expression but also a challenging task in the field of computational biology. Traditional methods for DPB prediction that depend on manually extracted features may lead to classification errors. Recently, deep learning such as convolutional neural network (CNN) has been successfully applied to classification tasks and improved DPB prediction performance significantly.

Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention.

Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues.

An integrated deep learning framework for joint segmentation of blood pool and myocardium.

Simultaneous and automatic segmentation of the blood pool and myocardium is an important precondition for early diagnosis and pre-operative planning in patients with complex congenital heart disease. However, due to the high diversity of cardiovascular structures and changes in mechanical properties caused by cardiac defects, the segmentation task still faces great challenges. To overcome these challenges, in this study we propose an integrated multi-task deep learning framework based on the dilated residual and hybrid pyramid pooling network (DRHPPN) for joint segmentation of the blood pool and myocardium.

Segmentation and visualization of left atrium through a unified deep learning framework.

Purpose: Left atrium segmentation and visualization serve as a fundamental and crucial role in clinical analysis and understanding of atrial fibrillation. However, most of the existing methods are directly transmitting information, which may cause redundant information to be passed to affect segmentation performance. Moreover, they did not further consider atrial visualization after segmentation, which leads to a lack of understanding of the essential atrial anatomy.

DeepCQ: Deep multi-task conditional quantification network for estimation of left ventricle parameters.

Background And Objective: Automatic cardiac left ventricle (LV) quantification plays an important role in assessing cardiac function. Although many advanced methods have been put forward to quantify related LV parameters, automatic cardiac LV quantification is still a challenge task due to the anatomy construction complexity of heart.

Methods: In this work, we propose a novel deep multi-task conditional quantification learning model (DeepCQ) which contains Segmentation module, Quantification encoder, and Dynamic analysis module.

econvRBP: Improved ensemble convolutional neural networks for RNA binding protein prediction directly from sequence.

RNA binding proteins (RBPs) determine RNA process from synthesis to decay, which play a key role in RNA transport, translation and degradation. Therefore, exploring RBPs' function from the amino acid sequence using computational methods has become one of the momentous topics in genome annotation. However, there still have some challenges: (1) shallow feature: Although the sequence determines structure is self-evident, it is difficult to analyze the essential features from simple sequence.

MsDBP: Exploring DNA-Binding Proteins by Integrating Multiscale Sequence Information via Chou's Five-Step Rule.

DNA-binding proteins are crucial to alternative splicing, methylation, and the structural composition of the DNA. The existing experimental methods for identifying DNA-binding proteins are expensive and time-consuming; thus, it is necessary to develop a fast and accurate computational method to address the problem. In this Article, we report a novel predictor MsDBP, a DNA-binding protein prediction method that combines the multiscale sequence feature into a deep neural network.

An integration of deep learning with feature embedding for protein-protein interaction prediction.

Protein-protein interactions are closely relevant to protein function and drug discovery. Hence, accurately identifying protein-protein interactions will help us to understand the underlying molecular mechanisms and significantly facilitate the drug discovery. However, the majority of existing computational methods for protein-protein interactions prediction are focused on the feature extraction and combination of features and there have been limited gains from the state-of-the-art models.

Cardiac-DeepIED: Automatic Pixel-Level Deep Segmentation for Cardiac Bi-Ventricle Using Improved End-to-End Encoder-Decoder Network.

Accurate segmentation of cardiac bi-ventricle (CBV) from magnetic resonance (MR) images has a great significance to analyze and evaluate the function of the cardiovascular system. However, the complex structure of CBV image makes fully automatic segmentation as a well-known challenge. In this paper, we propose an improved end-to-end encoder-decoder network for CBV segmentation from the pixel level view (Cardiac-DeepIED).

Direct Segmentation-Based Full Quantification for Left Ventricle via Deep Multi-Task Regression Learning Network.

Quantitative analysis of the heart is extremely necessary and significant for detecting and diagnosing heart disease, yet there are still some challenges. In this study, we propose a new end-to-end segmentation-based deep multi-task regression learning model (Indices-JSQ) to make a holonomic quantitative analysis of the left ventricle (LV), which contains a segmentation network (Img2Contour) and multi-task regression network (Contour2Indices). First, Img2Contour, which contains a deep convolutional encoder-decoder module, is designed to obtain the LV contour.

Classifying Incomplete Gene-Expression Data: Ensemble Learning with Non-Pre-Imputation Feature Filtering and Best-First Search Technique.

(1) Background: Gene-expression data usually contain missing values (MVs). Numerous methods focused on how to estimate MVs have been proposed in the past few years. Recent studies show that those imputation algorithms made little difference in classification.