In this paper, we propose a joint segmentation and classification framework for sentence-level sentiment classification. It is widely recognized that phrasal information is crucial for sentiment classification. Howeve...
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In this paper, we propose a joint segmentation and classification framework for sentence-level sentiment classification. It is widely recognized that phrasal information is crucial for sentiment classification. However, existing sentiment classification algorithms typically split a sentence as a word sequence, which does not effectively handle the inconsistent sentiment polarity between a phrase and the words it contains, such as {"not bad," "bad"} and {"a great deal of," "great"}. We address this issue by developing a joint framework for sentence-level sentiment classification. It simultaneously generates useful segmentations and predicts sentence-level polarity based on the segmentation results. Specifically, we develop a candidate generation model to produce segmentation candidates of a sentence;a segmentation ranking model to score the usefulness of a segmentation candidate for sentiment classification;and a classification model for predicting the sentiment polarity of a segmentation. We train the joint framework directly from sentences annotated with only sentiment polarity, without using any syntactic or sentiment annotations in segmentation level. We conduct experiments for sentiment classification on two benchmark datasets: a tweet dataset and a review dataset. Experimental results show that: 1) our method performs comparably with state-of-the-art methods on both datasets;2) joint modeling segmentation and classification outperforms pipelined baseline methods in various experimental settings.
Diabetic retinopathy (DR) is the leading cause of blindness among people of working age. Fundus lesions are clinical signs of DR, and their recognition and delineation are important for early screening, grading, and m...
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Diabetic retinopathy (DR) is the leading cause of blindness among people of working age. Fundus lesions are clinical signs of DR, and their recognition and delineation are important for early screening, grading, and monitoring of the disease. We propose in this work a fully automatic deep convolutional neural network method for simultaneous segmentation of four different types of DR-related fundus lesions. To exploit multi-scale image information, we propose a collaborative architecture that comprises a contextual branch and a local branch. An attention mechanism is designed to fuse feature maps from all decoding layers in order to effectively and fully combine informative features from the two branches. Moreover, an auxiliary classification task with a novel supervision scheme is introduced to reduce model overfitting and further improve the accuracy of lesion segmentation. Extensive experiments are conducted using three public fundus datasets, and our method produces a mean AUC value of 0.677, 0.629, and 0.581 on them respectively. The results demonstrate the advantages of the proposed method, outperforming alternative strategies and other state-of-the-art methods in the literature.(c) 2023 Elsevier B.V. All rights reserved.
Background and objective: The diagnosis of BI-RADS category 4 breast lesion is difficult because its prob-ability of malignancy ranges from 2% to 95%. For BI-RADS category 4 breast lesions, MRI is one of the prominent...
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Background and objective: The diagnosis of BI-RADS category 4 breast lesion is difficult because its prob-ability of malignancy ranges from 2% to 95%. For BI-RADS category 4 breast lesions, MRI is one of the prominent noninvasive imaging techniques. In this paper, we research computer algorithms to segment lesions and classify the benign or malignant lesions in MRI images. However, this task is challenging because the BI-RADS category 4 lesions are characterized by irregular shape, imbalanced class, and low ***: We fully utilize the intrinsic correlation between segmentation and classification tasks, where accurate segmentation will yield accurate classification results, and classification results will promote better segmentation. Therefore, we propose a collaborative multi-task algorithm (CMTL-SC). Specifically, a preliminary segmentation subnet is designed to identify the boundaries, locations and segmentation masks of lesions;a classification subnet, which combines the information provided by the preliminary segmentation, is designed to achieve benign or malignant classification;a repartition segmentation sub -net which aggregates the benign or malignant results, is designed to refine the lesion segment. The three subnets work cooperatively so that the CMTL-SC can identify the lesions better which solves the three *** and conclusion: We collect MRI data from 248 patients in the Second Hospital of Dalian Medi-cal University. The results show that the lesion boundaries delineated by the CMTL-SC are close to the boundaries delineated by the physicians. Moreover, the CMTL-SC yields better results than the single-task and multi-task state-of-the-art algorithms. Therefore, CMTL-SC can help doctors make precise diagnoses and refine treatments for patients.& COPY;2023 Published by Elsevier B.V.
Automated quality control of pavement and concrete surfaces is essential for maintaining structural integrity and consistency in the construction and infrastructure industries. This paper presents a novel deep learnin...
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Automated quality control of pavement and concrete surfaces is essential for maintaining structural integrity and consistency in the construction and infrastructure industries. This paper presents a novel deep learning model designed for automated quality control of these surfaces during both construction and maintenance phases. The model employs per-pixel segmentation and per-image classification, integrating both local and broader context information. Additionally, we utilize the classification results to improve segmentation during both training and inference stages. We evaluated the proposed model on a publicly available dataset containing more than 7,000 images of pavement and concrete cracks. The model achieved a Dice score of 81% and an intersection-over-union of 71%, surpassing publicly available state-of-the-art methods by at least 6-7 percentage points. An ablation study confirms that leveraging classification information enhances overall segmentation performance. Furthermore, our model is computationally efficient, processing over 30 FPS for 512 x 512 images, making it suitable for real-time applications on medium-resolution images. Code and the corrected dataset ground truths are publicly available: https://***/vicoslab/***.
Background and purpose: MRI images timely and accurately reflect ischemic injuries to the brain tissues and, therefore, can support clinical decision-making of acute ischemic stroke (AIS). To maximize the information ...
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Background and purpose: MRI images timely and accurately reflect ischemic injuries to the brain tissues and, therefore, can support clinical decision-making of acute ischemic stroke (AIS). To maximize the information provided by the MRI images, we leverage deep learning models to segment, classify, and map lesion distributions of ***: We evaluated brain MRI images of AIS patients from 2017 to 2020 at a tertiary teaching hospital and developed the Semantic segmentation Guided Detector Network (SGD-Net), composed of the first U-shaped model for segmentation in diffusion-weighted imaging (DWI) and the second model for binary classification of lesion size (lacune vs. non-lacune) and circulatory territory of lesion location (anterior vs. posterior circulation). Next, we modified the two-stage deep learning model into SGD-Net Plus by automatically segmenting AIS lesions in DWI images and registering the lesion in T1-weighted images and the brain atlases. Results: The final enrollment (216 patients with 4606 slices) was divided into 80% for model development and 20% for testing. S1 model segmented AIS lesions in DWI images accurately with a pixel accuracy > 99% (Dice 0.806-0.828 and IoU 0.675-707). In comprehensive evaluation of classification performance, the two-stage SGDNet outperformed the traditional one-stage models in classifying AIS lesion size (accuracy 0.867-0.956 vs. 0.511-0.867, AUROC 0.962-0.992 vs. 0.528-0.937, AUPRC 0.964-0.994 vs. 0.549-0.938) and location (accuracy 0.860-0.930 vs. 0.326-0.721, AUROC 0.936-0.988 vs. 0.493-0.833, AUPRC 0.883-0.978 vs. 0.365-0.695). The precise lesion segmentation at the first stage of the deep learning model was the basis for further application. After that, the modified two-stage model SGD-Net Plus accurately reported the volume, region percentage, and Abbreviations: AIS, Acute Ischemic Stroke;MRI, Magnetic Resonance Imaging;PACS, Picture Archiving , Communication System;DWI, Diffusion-Weighted Imaging;T1W, T1-Weigh
Hidden Markov models (HMMs) provide joint segmentation and classification of sequential data by efficient inference algorithms and have therefore been employed in fields as diverse as speech recognition, document proc...
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Hidden Markov models (HMMs) provide joint segmentation and classification of sequential data by efficient inference algorithms and have therefore been employed in fields as diverse as speech recognition, document processing, and genomics. However, conventional HMMs do not suit action segmentation in video due to the nature of the measurements which are often irregular in space and time, high dimensional and affected by outliers. For this reason, in this paper we present a joint action segmentation and classification approach based on an extended model: the hidden Markov model for multiple, irregular observations (HMM-MIO). Experiments performed over a concatenated version of the popular KTH action dataset and the challenging CMU multi-modal activity dataset (CMU-MMAC) report accuracies comparable to or higher than those of a bag-of-features approach, showing the usefulness of improved sequential models for joint action segmentation and classification tasks.
We propose an action parsing algorithm to parse a video sequence containing an unknown number of actions into its action segments. We argue that context information, particularly the temporal information about other a...
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ISBN:
(纸本)9781538628393
We propose an action parsing algorithm to parse a video sequence containing an unknown number of actions into its action segments. We argue that context information, particularly the temporal information about other actions in the video sequence, is valuable for action segmentation. The proposed parsing algorithm temporally segments the video sequence into action segments. The optimal temporal segmentation is found using a dynamic programming search algorithm that optimizes the overall classification confidence score. The classification score of each segment is determined using local features calculated from that segment as well as context features calculated from other candidate action segments of the sequence. Experimental results on the Breakfast activity data-set showed improved segmentation accuracy compared to existing state-of-the-art parsing techniques.
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