pwc-archive/papers-with-abstracts huggingface dataset:
Globe230k: A Benchmark Dense-Pixel Annotation Dataset for Global Land Cover Mapping
Authors: Qian  Shi, Da  He, Zhengyu  Liu, Xiaoping  Liu, Jingqian  Xue
Date: 2023-08-16 00:00:00
Proceeding: journal of remote sensing 2023 8
Tasks: Diversity, Learning Theory, Segmentation, Semantic Segmentation

Global land cover map provides fundamental information for understanding the relationship between global 
environmental change and human settlement. With the development of data-driven deep learning theory, 
semantic segmentation network has largely facilitated the global land cover mapping activity. However, the 
performance of semantic segmentation network is closely related to the number and quality of training 
data, and the existing annotation data are usually insufficient in quantity, quality, and spatial resolution, and 
are usually sampled at local region and lack diversity and variability, making data-driven model difficult to 
extend to global scale. Therefore, we proposed a large-scale annotation dataset (Globe230k) for semantic 
segmentation of remote sensing image, which has 3 superiorities: (a) large scale: the Globe230k dataset 
includes 232,819 annotated images with a size of 512 × 512 and a spatial resolution of 1 m, including 10 firstlevel categories; (b) rich diversity: the annotated images are sampled from worldwide regions, with coverage 
area of over 60,000 km2
, indicating a high variability and diversity; (c) multimodal: the Globe230k dataset 
not only contains RGB bands but also includes other important features for Earth system research, such as 
normalized differential vegetation index (NDVI), digital elevation model (DEM), vertical–vertical polarization 
(VV) bands, and vertical–horizontal polarization (VH) bands, which can facilitate the multimodal data fusion 
research. We used the Globe230k dataset to test several state-of-the-art semantic segmentation algorithms 
and found that it is able to evaluate algorithms in multiple aspects that are crucial for characterizing land 
covers, including multiscale modeling, detail reconstruction, and generalization ability. The dataset has 
been made public and can be used as a benchmark to promote further development of global land cover 
mapping and semantic segmentation algorithm development.
Using the Choquet Integral in the Pooling Layer in Deep Learning Networks
Authors: Camila Alves Dias1(&), Jéssica C. S. Bueno2, Eduardo N. Borges1, Silvia S. C. Botelho1, Graçaliz Pereira Dimuro1, 2, Giancarlo Lucca3, Javier Fernandéz3, Humberto Bustince3, 4, and Paulo Lilles Jorge Drews Junior1
Date: 2022-05-06 00:00:00
Proceeding: elsvier 2022 5

This paper aims to introduce the proposal of replacing the usual pooling functions by the Choquet integral in Deep Learning Networks. The Choquet integral is an aggregation function studied and applied in several areas, as, e.g., in classification problems. Its importance is related to the fact that it considers the relationship between the data to be aggregated by means of a fuzzy measure, unlike other aggregation functions such as the arithmetic mean and the maximum. The idea of this paper is to use the Choquet integral to reduce the size of an image, obtaining an abstract form of representation, that is, reducing the perception of the network corresponding to small changes in the image. The use of this aggregation function in the place of the max-pooling and mean-pooling functions of Convolutional Neural Networks presented promising results. This assertion is based on the Normalized Cross-Correlation and Structural Content quality measures applied to the original images and resulting images. It is important to emphasize that this preliminary study of Choquet integral as a pool layer has not yet been implemented on Convolutional Neural Networks until the present moment.
Exploiting Patch Sizes and Resolutions for Multi-Scale Deep Learning in Mammogram Image Classification
Authors: Gonzalo Iñaki Quintana, Zhijin Li, Laurence Vancamberg, Mathilde Mougeot, Agnès Desolneux and Serge Muller
Date: 2023-04-21 00:00:00
Proceeding: Bioengineering 2023 4
Tasks: Binary Classification, Cancer-no cancer per image classification, image-classification, Image Classification

Recent progress in deep learning (DL) has revived the interest on DL-based computer aided detection or diagnosis (CAD) systems for breast cancer screening. Patch-based approaches are one of the main state-of-the-art techniques for 2D mammogram image classification, but they are intrinsically limited by the choice of patch size, as there is no unique patch size that is adapted to all lesion sizes. In addition, the impact of input image resolution on performance is not yet fully understood. In this work, we study the impact of patch size and image resolution on the classifier performance for 2D mammograms. To leverage the advantages of different patch sizes and resolutions, a multi patch-size classifier and a multi-resolution classifier are proposed. These new architectures perform multi-scale classification by combining different patch sizes and input image resolutions. The AUC is increased by 3% on the public CBIS-DDSM dataset and by 5% on an internal dataset. Compared with a baseline single patch size and single resolution classifier, our multi-scale classifier reaches an AUC of 0.809 and 0.722 in each dataset.
A sensor fusion system with thermal infrared camera and LiDAR for autonomous vehicles and deep learning based object detection
Authors: Ji Dong Choi, Min Young Kim∗
Date: 2022-01-05 00:00:00
Proceeding: ICT Express 2022 1
Tasks: Autonomous Vehicles, object-detection, Object Detection, Sensor Fusion

Vision, Radar, and LiDAR sensors are widely used for autonomous vehicle perception technology. Especially object detection and classification are primarily dependent on vision sensors. However, under poor lighting conditions, dazzling sunlight, or bad weather an object might be difficult to be identified with general vision sensors. In this paper, we propose a sensor fusion system that combines a thermal infrared camera and a LiDAR sensor that can reliably detect and identify objects even in environments with poor visibility, such as day or night. The proposed method obtains the external parameters of the two sensors by designing and manufacturing a 3D calibration target to externally calibrate the thermal infrared camera and the LiDAR sensor. To verify the performance, experiments were conducted in day and night environments. The proposed sensor system and fusion algorithm show that it can reliably detect and identify objects even in environments with poor visibility, such as day or night.
Machine Learning Algorithms for Breast Cancer Detection in Mammography Images: A Comparative Study
Authors: Rhaylander Mendes de Miranda Almeida, Dehua Chen, Agnaldo Lopes da Silva Filho, Wladmir Cardoso Brandao
Date: 2021-04-26 00:00:00
Proceeding: 23rd International Conference on Enterprise Information Systems (ICEIS) 2021 4
Tasks: Breast Cancer Detection, Cancer-no cancer per image classification, Data Augmentation, Diversity, Medical Image Analysis, Prognosis, Transfer Learning

Breast tumor is the most common type of cancer in women worldwide, representing approximately 12% of
reported new cases and 6.5% of cancer deaths in 2018. Mammography screening are extremely important for
early detection of breast cancer. The assessment of mammograms is a complex task with significant variability
due to professional experience and human errors, an opportunity for assisting tools to improve both reliability
and accuracy. The usage of deep learning in medical image analysis have increased, assisting specialists in
early detection, diagnosis, treatment or prognosis of diseases. In this article, we compare the performance of
XGBoost and VGG16 in the task of breast cancer detection by using digital mammograms from CBIS-DDSM
dataset. In addition, we perform a comparison of prediction accuracy between full mammogram images
and patches extracted from original images based on ROI annotated by experts. Moreover, we also perform
experiments with transfer learning and data augmentation to exploit data diversity, and the ability to extract
features and learn from raw unprocessed data. Experimental results show that XGBoost achieves 68.29% in
AUC, while VGG16 achieves approximately the same performance of 68.24% in AUC
INbreast: toward a full-field digital mammographic database
Authors: Ines C. Moreira, Igor Amaral, Ines Domingues, Antonio Cardoso, Maria Joao Cardoso, Jaime S. Cardoso
Date: 2012-02-19 00:00:00
Proceeding: Elsevier 2012 2
Tasks: Ethics

Rationale and objectives: Computer-aided detection and diagnosis (CAD) systems have been developed in the past two decades to assist radiologists in the detection and diagnosis of lesions seen on breast imaging exams, thus providing a second opinion. Mammographic databases play an important role in the development of algorithms aiming at the detection and diagnosis of mammary lesions. However, available databases often do not take into consideration all the requirements needed for research and study purposes. This article aims to present and detail a new mammographic database.

Materials and methods: Images were acquired at a breast center located in a university hospital (Centro Hospitalar de S. João [CHSJ], Breast Centre, Porto) with the permission of the Portuguese National Committee of Data Protection and Hospital's Ethics Committee. MammoNovation Siemens full-field digital mammography, with a solid-state detector of amorphous selenium was used.

Results: The new database-INbreast-has a total of 115 cases (410 images) from which 90 cases are from women with both breasts affected (four images per case) and 25 cases are from mastectomy patients (two images per case). Several types of lesions (masses, calcifications, asymmetries, and distortions) were included. Accurate contours made by specialists are also provided in XML format.

Conclusion: The strengths of the actually presented database-INbreast-relies on the fact that it was built with full-field digital mammograms (in opposition to digitized mammograms), it presents a wide variability of cases, and is made publicly available together with precise annotations. We believe that this database can be a reference for future works centered or related to breast cancer imaging.
WDCCNet: Weighted Double-Classifier Constraint Neural Network for Mammographic Image Classification
Authors: Yan Wang, Zizhou Wang, Yangqin Feng, Lei Zhang
Date: 2021-10-04 00:00:00
Proceeding: IEEE Transactions on Medical Imaging 2021 10
Tasks: image-classification, Image Classification, Suspicous (BIRADS 4,5)-no suspicous (BIRADS 1,2,3) per image classification

The early detection and timely treatment of breast cancer can save lives. Mammography is one of the most efficient approaches to screening early breast cancer. An automatic mammographic image classification method could improve the work efficiency of radiologists. Current deep learning-based methods typically use the traditional softmax loss to optimize the feature extraction part, which aims to learn the features of mammographic images. However, previous studies have shown that the feature extraction part cannot learn discriminative features from complex data using the standard softmax loss. In this paper, we design a new architecture and propose respective loss functions. Specifically, we develop a double-classifier network architecture that constrains the extracted features’ distribution by changing the classifiers’ decision boundaries. Then, we propose the double-classifier constraint loss function to constrain the decision boundaries so that the feature extraction part can learn discriminative features. Furthermore, by taking advantage of the architecture of two classifiers, the neural network can detect the difficult-to-classify samples. We propose a weighted double-classifier constraint method to make the feature extract part pay more attention to learning difficult-to-classify samples’ features. Our proposed method can be easily applied to an existing convolutional neural network to improve mammographic image classification performance. We conducted extensive experiments to evaluate our methods on three public benchmark mammographic image datasets. The results showed that our methods outperformed many other similar methods and state-of-the-art methods on the three public medical benchmarks. Our code and weights can be found on GitHub.
Deep Neural Networks With Region-Based Pooling Structures for Mammographic Image Classification
Authors: Xin Shu; Lei Zhang; Zizhou Wang; Qing Lv; Zhang Yi
Date: 2020-06-06 00:00:00
Proceeding: IEEE Transactions on Medical Imaging 2020 6
Tasks: Diagnostic, image-classification, Image Classification, Segmentation, Suspicous (BIRADS 4,5)-no suspicous (BIRADS 1,2,3) per image classification

Breast cancer is one of the most frequently diagnosed solid cancers. Mammography is the most commonly used screening technology for detecting breast cancer. Traditional machine learning methods of mammographic image classification or segmentation using manual features require a great quantity of manual segmentation annotation data to train the model and test the results. But manual labeling is expensive, time-consuming, and laborious, and greatly increases the cost of system construction. To reduce this cost and the workload of radiologists, an end-to-end full-image mammogram classification method based on deep neural networks was proposed for classifier building, which can be constructed without bounding boxes or mask ground truth label of training data. The only label required in this method is the classification of mammographic images, which can be relatively easy to collect from diagnostic reports. Because breast lesions usually take up a fraction of the total area visualized in the mammographic image, we propose different pooling structures for convolutional neural networks(CNNs) instead of the common pooling methods, which divide the image into regions and select the few with high probability of malignancy as the representation of the whole mammographic image. The proposed pooling structures can be applied on most CNN-based models, which may greatly improve the models' performance on mammographic image data with the same input. Experimental results on the publicly available INbreast dataset and CBIS dataset indicate that the proposed pooling structures perform satisfactorily on mammographic image data compared with previous state-of-the-art mammographic image classifiers and detection algorithm using segmentation annotations.
Automated mass detection in mammograms using cascaded deep learning and random forests,
Authors: Neeraj Dhungel; Gustavo Carneiro; Andrew P. Bradley
Date: 2015-11-23 00:00:00
Proceeding: International Conference on Digital Image Computing: Techniques and Applications (DICTA) 2015 11
Tasks: Suspicous (BIRADS 4,5)-no suspicous (BIRADS 1,2,3) per image classification

Mass detection from mammograms plays a crucial role as a pre- processing stage for mass segmentation and classification. The detection of masses from mammograms is considered to be a challenging problem due to their large variation in shape, size, boundary and texture and also because of their low signal to noise ratio compared to the surrounding breast tissue. In this paper, we present a novel approach for detecting masses in mammograms using a cascade of deep learning and random forest classifiers. The first stage classifier consists of a multi-scale deep belief network that selects suspicious regions to be further processed by a two-level cascade of deep convolutional neural networks. The regions that survive this deep learning analysis are then processed by a two-level cascade of random forest classifiers that use morphological and texture features extracted from regions selected along the cascade. Finally, regions that survive the cascade of random forest classifiers are combined using connected component analysis to produce state-of-the-art results. We also show that the proposed cascade of deep learning and random forest classifiers are effective in the reduction of false positive regions, while maintaining a high true positive detection rate. We tested our mass detection system on two publicly available datasets: DDSM-BCRP and INbreast. The final mass detection produced by our approach achieves the best results on these publicly available datasets with a true positive rate of 0.96 ± 0.03 at 1.2 false positive per image on INbreast and true positive rate of 0.75 at 4.8 false positive per image on DDSM-BCRP.
Sparse Bayesian Multidimensional Item Response Theory
Authors: Jiguang Li, Robert Gibbons, Veronika Rockova
Date: 2023-10-26 00:00:00

Multivariate Item Response Theory (MIRT) is sought-after widely by applied researchers looking for interpretable (sparse) explanations underlying response patterns in questionnaire data. There is, however, an unmet demand for such sparsity discovery tools in practice. Our paper develops a Bayesian platform for binary and ordinal item MIRT which requires minimal tuning and scales well on large datasets due to its parallelizable features. Bayesian methodology for MIRT models has traditionally relied on MCMC simulation, which cannot only be slow in practice, but also often renders exact sparsity recovery impossible without additional thresholding. In this work, we develop a scalable Bayesian EM algorithm to estimate sparse factor loadings from mixed continuous, binary, and ordinal item responses. We address the seemingly insurmountable problem of unknown latent factor dimensionality with tools from Bayesian nonparametrics which enable estimating the number of factors. Rotations to sparsity through parameter expansion further enhance convergence and interpretability without identifiability constraints. In our simulation study, we show that our method reliably recovers both the factor dimensionality as well as the latent structure on high-dimensional synthetic data even for small samples. We demonstrate the practical usefulness of our approach on three datasets: an educational assessment dataset, a quality-of-life measurement dataset, and a bio-behavioral dataset. All demonstrations show that our tool yields interpretable estimates, facilitating interesting discoveries that might otherwise go unnoticed under a pure confirmatory factor analysis setting.
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews
Authors: Xintao Wang, Yunze Xiao, Jen-tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, Yanghua Xiao
Date: 2023-10-27 00:00:00

Role-playing agents (RPAs), powered by large language models, have emerged as a flourishing field of applications. However, a key challenge lies in assessing whether RPAs accurately reproduce the personas of target characters, namely their character fidelity. Existing methods mainly focus on the knowledge and linguistic patterns of characters. This paper, instead, introduces a novel perspective to evaluate the personality fidelity of RPAs with psychological scales. Overcoming drawbacks of previous self-report assessments on RPAs, we propose InCharacter, namely Interviewing Character agents for personality tests. Experiments include various types of RPAs and LLMs, covering 32 distinct characters on 14 widely used psychological scales. The results validate the effectiveness of InCharacter in measuring RPA personalities. Then, with InCharacter, we show that state-of-the-art RPAs exhibit personalities highly aligned with the human-perceived personalities of the characters, achieving an accuracy up to 80.7%.
Gen2Sim: Scaling up Robot Learning in Simulation with Generative Models
Authors: Pushkal Katara, Zhou Xian, Katerina Fragkiadaki
Date: 2023-10-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

Generalist robot manipulators need to learn a wide variety of manipulation skills across diverse environments. Current robot training pipelines rely on humans to provide kinesthetic demonstrations or to program simulation environments and to code up reward functions for reinforcement learning. Such human involvement is an important bottleneck towards scaling up robot learning across diverse tasks and environments. We propose Generation to Simulation (Gen2Sim), a method for scaling up robot skill learning in simulation by automating generation of 3D assets, task descriptions, task decompositions and reward functions using large pre-trained generative models of language and vision. We generate 3D assets for simulation by lifting open-world 2D object-centric images to 3D using image diffusion models and querying LLMs to determine plausible physics parameters. Given URDF files of generated and human-developed assets, we chain-of-thought prompt LLMs to map these to relevant task descriptions, temporal decompositions, and corresponding python reward functions for reinforcement learning. We show Gen2Sim succeeds in learning policies for diverse long horizon tasks, where reinforcement learning with non temporally decomposed reward functions fails. Gen2Sim provides a viable path for scaling up reinforcement learning for robot manipulators in simulation, both by diversifying and expanding task and environment development, and by facilitating the discovery of reinforcement-learned behaviors through temporal task decomposition in RL. Our work contributes hundreds of simulated assets, tasks and demonstrations, taking a step towards fully autonomous robotic manipulation skill acquisition in simulation.
A Stability Principle for Learning under Non-Stationarity
Authors: Chengpiao Huang, Kaizheng Wang
Date: 2023-10-27 00:00:00

We develop a versatile framework for statistical learning in non-stationary environments. In each time period, our approach applies a stability principle to select a look-back window that maximizes the utilization of historical data while keeping the cumulative bias within an acceptable range relative to the stochastic error. Our theory showcases the adaptivity of this approach to unknown non-stationarity. We prove regret bounds that are minimax optimal up to logarithmic factors when the population losses are strongly convex, or Lipschitz only. At the heart of our analysis lie two novel components: a measure of similarity between functions and a segmentation technique for dividing the non-stationary data sequence into quasi-stationary pieces. We evaluate the practical performance of our approach through real-data experiments on electricity demand prediction and hospital nurse staffing.
Socially Cognizant Robotics for a Technology Enhanced Society
Authors: Kristin J. Dana, Clinton Andrews, Kostas Bekris, Jacob Feldman, Matthew Stone, Pernille Hemmer, Aaron Mazzeo, Hal Salzman, Jingang Yi
Date: 2023-10-27 00:00:00

Emerging applications of robotics, and concerns about their impact, require the research community to put human-centric objectives front-and-center. To meet this challenge, we advocate an interdisciplinary approach, socially cognizant robotics, which synthesizes technical and social science methods. We argue that this approach follows from the need to empower stakeholder participation (from synchronous human feedback to asynchronous societal assessment) in shaping AI-driven robot behavior at all levels, and leads to a range of novel research perspectives and problems both for improving robots' interactions with individuals and impacts on society. Drawing on these arguments, we develop best practices for socially cognizant robot design that balance traditional technology-based metrics (e.g. efficiency, precision and accuracy) with critically important, albeit challenging to measure, human and society-based metrics.