Mitigating the Impact of False Negatives in Dense Retrieval with Contrastive Confidence Regularization

In open-domain Question Answering (QA), dense retrieval is crucial for finding relevant passages for answer generation. Typically, contrastive learning is used to train a retrieval model that maps passages and queries to the same semantic space. The objective is to make similar ones closer and dissimilar ones further apart. However, training such a system is challenging due to the false negative issue, where relevant passages may be missed during data annotation. Hard negative sampling, which is commonly used to improve contrastive learning, can introduce more noise in training. This is because hard negatives are those closer to a given query, and thus more likely to be false negatives. To address this issue, we propose a novel contrastive confidence regularizer for Noise Contrastive Estimation (NCE) loss, a commonly used loss for dense retrieval. Our analysis shows that the regularizer helps dense retrieval models be more robust against false negatives with a theoretical guarantee. Additionally, we propose a model-agnostic method to filter out noisy negative passages in the dataset, improving any downstream dense retrieval models. Through experiments on three datasets, we demonstrate that our method achieves better retrieval performance in comparison to existing state-of-the-art dense retrieval systems.Comment: Accepted by AAAI2

CT Male Pelvic Organ Segmentation via Hybrid Loss Network With Incomplete Annotation

Sufficient data with complete annotation is essential for training deep models to perform automatic and accurate segmentation of CT male pelvic organs, especially when such data is with great challenges such as low contrast and large shape variation. However, manual annotation is expensive in terms of both finance and human effort, which usually results in insufficient completely annotated data in real applications. To this end, we propose a novel deep framework to segment male pelvic organs in CT images with incomplete annotation delineated in a very user-friendly manner. Specifically, we design a hybrid loss network derived from both voxel classification and boundary regression, to jointly improve the organ segmentation performance in an iterative way. Moreover, we introduce a label completion strategy to complete the labels of the rich unannotated voxels and then embed them into the training data to enhance the model capability. To reduce the computation complexity and improve segmentation performance, we locate the pelvic region based on salient bone structures to focus on the candidate segmentation organs. Experimental results on a large planning CT pelvic organ dataset show that our proposed method with incomplete annotation achieves comparable segmentation performance to the state-of-the-art methods with complete annotation. Moreover, our proposed method requires much less effort of manual contouring from medical professionals such that an institutional specific model can be more easily established

Iterative Label Denoising Network: Segmenting Male Pelvic Organs in CT From 3D Bounding Box Annotations

Obtaining accurate segmentation of the prostate and nearby organs at risk (e.g., bladder and rectum) in CT images is critical for radiotherapy of prostate cancer. Currently, the leading automatic segmentation algorithms are based on Fully Convolutional Networks (FCNs), which achieve remarkable performance but usually need large-scale datasets with high-quality voxel-wise annotations for full supervision of the training. Unfortunately, such annotations are difficult to acquire, which becomes a bottleneck to build accurate segmentation models in real clinical applications. In this paper, we propose a novel weakly supervised segmentation approach that only needs 3D bounding box annotations covering the organs of interest to start the training. Obviously, the bounding box includes many non-organ voxels that carry noisy labels to mislead the segmentation model. To this end, we propose the label denoising module and embed it into the iterative training scheme of the label denoising network (LDnet) for segmentation. The labels of the training voxels are predicted by the tentative LDnet, while the label denoising module identifies the voxels with unreliable labels. As only the good training voxels are preserved, the iteratively re-trained LDnet can refine its segmentation capability gradually. Our results are remarkable, i.e., reaching ~ 94% (prostate), ~ 91% (bladder), and ~ 86% (rectum) of the Dice Similarity Coefficients (DSCs), compared to the case of fully supervised learning upon high-quality voxel-wise annotations and also superior to several state-of-the-art approaches. To our best knowledge, this is the first work to achieve voxel-wise segmentation in CT images from simple 3D bounding box annotations, which can greatly reduce many labeling efforts and meet the demands of the practical clinical applications

Long Short-Term Planning for Conversational Recommendation Systems

In Conversational Recommendation Systems (CRS), the central question is how the conversational agent can naturally ask for user preferences and provide suitable recommendations. Existing works mainly follow the hierarchical architecture, where a higher policy decides whether to invoke the conversation module (to ask questions) or the recommendation module (to make recommendations). This architecture prevents these two components from fully interacting with each other. In contrast, this paper proposes a novel architecture, the long short-term feedback architecture, to connect these two essential components in CRS. Specifically, the recommendation predicts the long-term recommendation target based on the conversational context and the user history. Driven by the targeted recommendation, the conversational model predicts the next topic or attribute to verify if the user preference matches the target. The balance feedback loop continues until the short-term planner output matches the long-term planner output, that is when the system should make the recommendation.Comment: 14 pages, 3 figures. Accepted by ICONIP 202

Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests

Segmenting male pelvic organs from CT images is a prerequisite for prostate cancer radiotherapy. The efficacy of radiation treatment highly depends on segmentation accuracy. However, accurate segmentation of male pelvic organs is challenging due to low tissue contrast of CT images, as well as large variations of shape and appearance of the pelvic organs. Among existing segmentation methods, deformable models are the most popular, as shape prior can be easily incorporated to regularize the segmentation. Nonetheless, the sensitivity to initialization often limits their performance, especially for segmenting organs with large shape variations. In this paper, we propose a novel approach to guide deformable models, thus making them robust against arbitrary initializations. Specifically, we learn a displacement regressor, which predicts 3D displacement from any image voxel to the target organ boundary based on the local patch appearance. This regressor provides a nonlocal external force for each vertex of deformable model, thus overcoming the initialization problem suffered by the traditional deformable models. To learn a reliable displacement regressor, two strategies are particularly proposed. 1) A multi-task random forest is proposed to learn the displacement regressor jointly with the organ classifier; 2) an auto-context model is used to iteratively enforce structural information during voxel-wise prediction. Extensive experiments on 313 planning CT scans of 313 patients show that our method achieves better results than alternative classification or regression based methods, and also several other existing methods in CT pelvic organ segmentation

Brain atlas fusion from high-thickness diagnostic magnetic resonance images by learning-based super-resolution

It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.e., with high inter-slice thickness), the problem of atlas fusion has not been addressed yet. In this paper, we intend to fuse the brain atlas from the high-thickness diagnostic MR images that are prevalent for clinical routines. The main idea of our works is to extend the conventional groupwise registration by incorporating a novel super-resolution strategy. The contribution of the proposed super-resolution framework is two-fold. First, each high-thickness subject image is reconstructed to be isotropic by the patch-based sparsity learning. Then, the reconstructed isotropic image is enhanced for better quality through the random-forest-based regression model. In this way, the images obtained by the super-resolution strategy can be fused together by applying the groupwise registration method to construct the required atlas. Our experiments have shown that the proposed framework can effectively solve the problem of atlas fusion from the low-quality brain MR images

Locally-constrained boundary regression for segmentation of prostate and rectum in the planning CT images

Automatic and accurate segmentation of the prostate and rectum in planning CT images is a challenging task due to low image contrast, unpredictable organ (relative) position, and uncertain existence of bowel gas across different patients. Recently, regression forest was adopted for organ deformable segmentation on 2D medical images by training one landmark detector for each point on the shape model. However, it seems impractical for regression forest to guide 3D deformable segmentation as a landmark detector, due to large number of vertices in the 3D shape model as well as the difficulty in building accurate 3D vertex correspondence for each landmark detector. In this paper, we propose a novel boundary detection method by exploiting the power of regression forest for prostate and rectum segmentation. The contributions of this paper are as follows: 1) we introduce regression forest as a local boundary regressor to vote the entire boundary of a target organ, which avoids training a large number of landmark detectors and building an accurate 3D vertex correspondence for each landmark detector; 2) an auto-context model is integrated with regression forest to improve the accuracy of the boundary regression; 3) we further combine a deformable segmentation method with the proposed local boundary regressor for the final organ segmentation by integrating organ shape priors. Our method is evaluated on a planning CT image dataset with 70 images from 70 different patients. The experimental results show that our proposed boundary regression method outperforms the conventional boundary classification method in guiding the deformable model for prostate and rectum segmentations. Compared with other state-of-the-art methods, our method also shows a competitive performance

Inhibitory/Suppressive oligodeoxynucleotide nanocapsules as simple oral delivery devices for preventing atopic dermatitis in mice

Advance online publication 6 January 2015Here, we report a simple and low-cost oral oligodeoxynucleotide (ODN) delivery system targeted to the gut Peyer's patches (PPs). This system requires only Dulbecco's modified eagle's medium, calcium chloride, ODNs, and basic laboratory equipment. ODN nanocapsules (ODNcaps) were directly delivered to the PPs through oral administration and were taken up by macrophages in the PPs, where they induced an immune response. Long-term continuous oral dosing with inhibitory/suppressive ODNcaps (iODNcaps, "iSG3caps" in this study) was evaluated using an atopic dermatitis mouse model to visually monitor disease course. Administration of iSG3caps improved skin lesions and decreased epidermal thickness. Underlying this effect is the ability of iSG3 to bind to and prevent phosphorylation of signal transducer and activator of transcription 6, thereby blocking the interleukin-4 signaling cascade mediated by binding of allergens to type 2 helper T cells. The results of our iSG3cap oral delivery experiments suggest that iSG3 may be useful for treating allergic diseases.ArticleMOLECULAR THERAPY. 23(2):297-309 (2015)journal articl

Modeling and Experimental Verification of an Electromagnetic and Piezoelectric Hybrid Energy Harvester

This paper describes mathematical models of an electromagnetic and piezoelectric hybrid energy harvesting system and provides an analysis of the relationship between the resonance frequency and the configuration parameters of the system. An electromagnetic and piezoelectric energy harvesting device was designed and the experimental results showed good agreement with the analytical results. The maximum load power of the hybrid energy harvesting system achieved 4.25 mW at a resonant frequency of 18 Hz when the acceleration was 0.7 g, which is an increase of 15% compared with the 3.62 mW achieved by a single electromagnetic technique

Correlation between hyperbilirubinemia risk and immune cell mitochondria parameters in neonates with jaundice

PurposeTo explore the correlation between mitochondria parameters of immune cells and hyperbilirubinemia risk in hospitalized neonates with jaundice.MethodsThis retrospective study included jaundiced neonates born between September 2020 and March 2022 at Shaoxing Keqiao Women & Children's Hospital. The neonates were divided into low, intermediate-low, intermediate-high, and high-risk groups according to the hyperbilirubinemia risk. The purpose parameters including percentage, absolute count, mitochondrial mass (MM), and single-cell MM (SCMM) of peripheral blood T lymphocytes detected by flow cytometry were collected.ResultsFinally, 162 neonates with jaundice (47, 41, 39, and 35 with low, intermediate-low, intermediate-high, and high-risk) were included. CD3+ SCMM was significantly higher in the high-risk group compared with the low and intermediate-low-risk groups (both P < 0.0083), CD4+ SCMM was significantly higher in the high-risk group compared with the three other groups (all P < 0.0083), and CD8+ SCMM was significantly higher in the intermediate-low and high-risk groups compared with the low-risk group (both P < 0.0083). CD3+ (r = 0.34, P < 0.001) and CD4+ (r = 0.20, P = 0.010) SCMM positively correlated with bilirubin levels.ConclusionsThe mitochondrial SCMM parameters differed significantly among jaundiced neonates with different hyperbilirubinemia risks. CD3+ and CD4+ T cell SCMM values were positively correlated with the serum bilirubin levels, and might correlated with hyperbilirubinemia risk