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Ensemble Attention Guided Multi-SEANet Trained with curriculum learning for Noninvasive Prediction of Gleason Grade Groups from MRI

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Journal of Shanghai Jiaotong university(Science) 2024年第1期29卷 109-119页

作者：沈傲胡冀苏金鹏飞周志勇钱旭升郑毅包婕王希明戴亚康 School of Computer Science and Technology Changchun University of Science and TechnologyChangchun 130022China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of SciencesSuzhou 215163JiangsuChina School of Biomedical Engineering(SooChow) University of Science and Technology of ChinaSuzhou 215163JiangsuChina Department of Radiology The First Afiliated Hospital of Soochow UniversitySuzhou 215006JiangsuChina

The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain pathology.To noninvasively evaluate GG,an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum learning.First,a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion area.Second,the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion area.Finally,in the network training,a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed,which further improves the prediction performance of the network.Ex-perimental results show that the proposed method is better than the traditional network model in predicting GG performance.The quadratic weighted Kappa is 0.4711 and the positive predictive value for predicting clinically significant cancer is 0.9369.

关键词： prostate cancer Gleason grade groups(GGs) bi-parametric magnetic resonance imaging deep learn-ing curriculum learning

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Boosting Unsupervised Domain Adaptation with Soft Pseudo-Label and curriculum learning

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Journal of Shanghai Jiaotong university(Science) 2023年第6期28卷 703-716页

作者：张晟嘉林天成徐奕 School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Key Laboratory of Digital Media Processing and Transmission Shanghai 200240China

By leveraging data from a fully labeled source domain,unsupervised domain adaptation(UDA)im-proves classification performance on an unlabeled target domain through explicit discrepancy minimization of data distribution or adversarial learning.As an enhancement,category alignment is involved during adaptation to reinforce target feature discrimination by utilizing model prediction.However,there remain unexplored prob-lems about pseudo-label inaccuracy incurred by wrong category predictions on target domain,and distribution deviation caused by overfitting on source domain.In this paper,we propose a model-agnostic two-stage learning framework,which greatly reduces flawed model predictions using soft pseudo-label strategy and avoids overfitting on source domain with a curriculum learning strategy.Theoretically,it successfully decreases the combined risk in the upper bound of expected error on the target domain.In the first stage,we train a model with distribution alignment-based UDA method to obtain soft semantic label on target domain with rather high confidence.To avoid overfitting on source domain,in the second stage,we propose a curriculum learning strategy to adaptively control the weighting between losses from the two domains so that the focus of the training stage is gradually shifted from source distribution to target distribution with prediction confidence boosted on the target domain.Extensive experiments on two well-known benchmark datasets validate the universal effectiveness of our proposed framework on promoting the performance of the top-ranked UDA algorithms and demonstrate its consistent su-perior performance.

关键词： unsupervised domain adaptation(UDA) pseudo-label soft label curriculum learning

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Text Difficulty Study:Do Machines Behave the Same as Humans Regarding Text Difficulty?

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Machine Intelligence Research 2024年第2期21卷 283-293页

作者： Bowen Chen Xiao Ding Yi Zhao Bo Fu Tingmao Lin Bing Qin Ting Liu Research Center for Social Computing and Information Retrieval Harbin Institute of TechnologyHarbin 150001China Fundamental Technology Center China Construction Bank Financial Technology Co.Ltd.Beijing 100033China

With the emergence of pre-trained models,current neural networks are able to give task performance that is comparable to humans.However,we know little about the fundamental working mechanism of pre-trained models in which we do not know how they approach such performance and how the task is solved by the model.For example,given a task,human learns from easy to hard,whereas the model learns randomly.Undeniably,difficulty-insensitive learning leads to great success in natural language processing(NLP),but little attention has been paid to the effect of text difficulty in NLP.We propose a human learning matching index(HLM Index)to investigate the effect of text difficulty.Experiment results show:1)LSTM gives more human-like learning behavior than BERT.Additionally,UID-SuperLinear gives the best evaluation of text difficulty among four text difficulty criteria.Among nine tasks,some tasks’performance is related to text difficulty,whereas others are not.2)Model trained on easy data performs best in both easy and medium test data,whereas trained on hard data only performs well on hard test data.3)Train the model from easy to hard,leading to quicker convergence.

关键词： Cognition inspired natural language processing psycholinguistics explainability text difficulty curriculum learning

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TSCL-SQL: Two-Stage curriculum learning Framework for Text-to-SQL

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Journal of Donghua University(English Edition) 2023年第4期40卷 421-427页

作者：尹枫程路易王秋月王志军杜明徐波 School of Computer Science and Technology Donghua UniversityShanghai 201620China

Text-to-SQL is the task of translating a natural language query into a structured query language. Existing text-to-SQL approaches focus on improving the model’s architecture while ignoring the relationship between queries and table schemas and the differences in difficulty between examples in the dataset. To tackle these challenges, a two-stage curriculum learning framework for text-to-SQL(TSCL-SQL) is proposed in this paper. To exploit the relationship between the queries and the table schemas, a schema identification pre-training task is proposed to make the model choose the correct table schema from a set of candidates for a specific query. To leverage the differences in difficulty between examples, curriculum learning is applied to the text-to-SQL task, accompanied by an automatic curriculum learning solution, including a difficulty scorer and a training scheduler. Experiments show that the framework proposed in this paper is effective.

关键词： text-to-SQL curriculum learning semantic parsing

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Visual Superordinate Abstraction for Robust Concept learning

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Machine Intelligence Research 2023年第1期20卷 79-91页

作者： Qi Zheng Chao-Yue Wang Dadong Wang Da-Cheng Tao University of Sydney Sydney 2008Australia JD Explore Academy Beijing 100176China DATA61 Commonwealth Scientific and Industrial Research OrganisationSydney 2122Australia

Concept learning constructs visual representations that are connected to linguistic semantics, which is fundamental to vision-language tasks. Although promising progress has been made, existing concept learners are still vulnerable to attribute perturbations and out-of-distribution compositions during inference. We ascribe the bottleneck to a failure to explore the intrinsic semantic hierarchy of visual concepts, e.g., {red, blue,···} ∈“color” subspace yet cube ∈“shape”. In this paper, we propose a visual superordinate abstraction framework for explicitly modeling semantic-aware visual subspaces(i.e., visual superordinates). With only natural visual question answering data, our model first acquires the semantic hierarchy from a linguistic view and then explores mutually exclusive visual superordinates under the guidance of linguistic hierarchy. In addition, a quasi-center visual concept clustering and superordinate shortcut learning schemes are proposed to enhance the discrimination and independence of concepts within each visual superordinate. Experiments demonstrate the superiority of the proposed framework under diverse settings, which increases the overall answering accuracy relatively by 7.5% for reasoning with perturbations and 15.6% for compositional generalization tests.

关键词： Concept learning visual question answering weakly-supervised learning multi-modal learning curriculum learning

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