scispace - formally typeset
Search or ask a question
Author

Xiaofeng Qi

Bio: Xiaofeng Qi is an academic researcher from Sichuan University. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 4, co-authored 6 publications receiving 106 citations.

Papers
More filters
Journal ArticleDOI
Xiaofeng Qi1, Lei Zhang1, Yao Chen1, Yong Pi1, Chen Yi1, Qing Lv1, Zhang Yi1 
TL;DR: An automated breast cancer diagnosis model for ultrasonography images using deep convolutional neural networks with multi‐scale kernels and skip connections is developed and achieves a performance comparable to human sonographers and can be applied to clinical scenarios.

148 citations

Journal ArticleDOI
Lituan Wang1, Lei Zhang1, Minjuan Zhu1, Xiaofeng Qi1, Zhang Yi1 
TL;DR: An attention-based feature aggregation network is proposed to automatically integrate the features extracted from multiple images in one examination, utilizing different views of the nodules to improve the performance of recognizing malignant nodules in the ultrasound images.

71 citations

Journal ArticleDOI
Yong Pi1, Yao Chen1, Dan Deng, Xiaofeng Qi1, Jilan Li1, Qing Lv1, Zhang Yi1 
TL;DR: This paper formulate the diagnosis of breast cancer on ultrasonography images as a Multiple Instance Learning (MIL) problem, diagnosing a breast nodule by jointly analyzing the nodule on multiple planes and develops an attention-augmented deep neural network to solve this problem.

13 citations

Journal ArticleDOI
TL;DR: Zhang et al. as mentioned in this paper proposed an attention-based imbalanced image classification (DAIIC) approach to automatically pay more attention to the minority classes in a data-driven manner, where an attention network and a novel attention augmented logistic regression function are employed to encapsulate as many features, which belongs to minority classes, as possible into the discriminative feature learning process.
Abstract: Class imbalance is a common problem in real-world image classification problems, some classes are with abundant data, and the other classes are not. In this case, the representations of classifiers are likely to be biased toward the majority classes and it is challenging to learn proper features, leading to unpromising performance. To eliminate this biased feature representation, many algorithm-level methods learn to pay more attention to the minority classes explicitly according to the prior knowledge of the data distribution. In this article, an attention-based approach called deep attention-based imbalanced image classification (DAIIC) is proposed to automatically pay more attention to the minority classes in a data-driven manner. In the proposed method, an attention network and a novel attention augmented logistic regression function are employed to encapsulate as many features, which belongs to the minority classes, as possible into the discriminative feature learning process by assigning the attention for different classes jointly in both the prediction and feature spaces. With the proposed object function, DAIIC can automatically learn the misclassification costs for different classes. Then, the learned misclassification costs can be used to guide the training process to learn more discriminative features using the designed attention networks. Furthermore, the proposed method is applicable to various types of networks and data sets. Experimental results on both single-label and multilabel imbalanced image classification data sets show that the proposed method has good generalizability and outperforms several state-of-the-art methods for imbalanced image classification.

12 citations

Proceedings ArticleDOI
29 Mar 2022
TL;DR: This work proposes a novel AAC system called CLIP-AAC to learn interactive cross-modality representation with both acoustic and textual information and indicates that both the pre-trained model and contrastive learning contribute to the performance gain of the AAC model.
Abstract: Automated Audio captioning (AAC) is a cross-modal task that generates natural language to describe the content of input audio. Most prior works usually extract single-modality acoustic features and are therefore sub-optimal for the cross-modal decoding task. In this work, we propose a novel AAC system called CLIP-AAC to learn interactive cross-modality representation with both acoustic and textual information. Specifically, the proposed CLIP-AAC introduces an audio-head and a text-head in the pre-trained encoder to extract audio-text information. Furthermore, we also apply contrastive learning to narrow the domain difference by learning the correspondence between the audio signal and its paired captions. Experimental results show that the proposed CLIP-AAC approach surpasses the best baseline by a significant margin on the Clotho dataset in terms of NLP evaluation metrics. The ablation study indicates that both the pre-trained model and contrastive learning contribute to the performance gain of the AAC model.

11 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: This study aims at presenting a review that shows the new applications of machine learning and deep learning technology for detecting and classifying breast cancer and provides an overview of progress and the future trends and challenges in the classification and detection of breast cancer.
Abstract: Breast cancer is the second leading cause of death for women, so accurate early detection can help decrease breast cancer mortality rates. Computer-aided detection allows radiologists to detect abnormalities efficiently. Medical images are sources of information relevant to the detection and diagnosis of various diseases and abnormalities. Several modalities allow radiologists to study the internal structure, and these modalities have been met with great interest in several types of research. In some medical fields, each of these modalities is of considerable significance. This study aims at presenting a review that shows the new applications of machine learning and deep learning technology for detecting and classifying breast cancer and provides an overview of progress in this area. This review reflects on the classification of breast cancer utilizing multi-modalities medical imaging. Details are also given on techniques developed to facilitate the classification of tumors, non-tumors, and dense masses in various medical imaging modalities. It first provides an overview of the different approaches to machine learning, then an overview of the different deep learning techniques and specific architectures for the detection and classification of breast cancer. We also provide a brief overview of the different image modalities to give a complete overview of the area. In the same context, this review was performed using a broad variety of research databases as a source of information for access to various field publications. Finally, this review summarizes the future trends and challenges in the classification and detection of breast cancer.

164 citations

Journal ArticleDOI
TL;DR: A novel method to segment the breast tumor via semantic classification and merging patches and achieved competitive results compared to conventional methods in terms of TP and FP, and produced good approximations to the hand-labelled tumor contours.

135 citations

Journal ArticleDOI
TL;DR: A selective kernel (SK) U-Net convolutional neural network to adjust network’s receptive fields via an attention mechanism, and fuse feature maps extracted with dilated and conventional convolutions for breast mass segmentation in ultrasound (US).

99 citations

Journal ArticleDOI
TL;DR: An overview of explainable artificial intelligence (XAI) used in deep learning-based medical image analysis can be found in this article , where a framework of XAI criteria is introduced to classify deep learning based medical image classification methods.

94 citations

Posted Content
TL;DR: An overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis is presented in this paper, where a framework of XAI criteria is introduced to classify deep learning based methods.
Abstract: With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outlook of future opportunities for XAI in medical image analysis.

92 citations