pwc-archive/papers-with-abstracts huggingface dataset:
EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation
Authors: Weiming Hu, Chen Li, Xiaoyan Li, Md Mamunur Rahaman, Yong Zhang, HaoYuan Chen, Wanli Liu, YuDong Yao, Hongzan Sun, Ning Xu, Xinyu Huang, Marcin Grzegorze
Date: 2022-02-17 00:00:00
Tasks: image-classification, Image Classification

Background and purpose: Colorectal cancer has become the third most common cancer worldwide, accounting for approximately 10% of cancer patients. Early detection of the disease is important for the treatment of colorectal cancer patients. Histopathological examination is the gold standard for screening colorectal cancer. However, the current lack of histopathological image datasets of colorectal cancer, especially enteroscope biopsies, hinders the accurate evaluation of computer-aided diagnosis techniques. Methods: A new publicly available Enteroscope Biopsy Histopathological H&E Image Dataset (EBHI) is published in this paper. To demonstrate the effectiveness of the EBHI dataset, we have utilized several machine learning, convolutional neural networks and novel transformer-based classifiers for experimentation and evaluation, using an image with a magnification of 200x. Results: Experimental results show that the deep learning method performs well on the EBHI dataset. Traditional machine learning methods achieve maximum accuracy of 76.02% and deep learning method achieves a maximum accuracy of 95.37%. Conclusion: To the best of our knowledge, EBHI is the first publicly available colorectal histopathology enteroscope biopsy dataset with four magnifications and five types of images of tumor differentiation stages, totaling 5532 images. We believe that EBHI could attract researchers to explore new classification algorithms for the automated diagnosis of colorectal cancer, which could help physicians and patients in clinical settings.
Anatomically Parameterized Statistical Shape Model: Explaining Morphometry through Statistical Learning
Authors: Arnaud Boutillon, Asma Salhi, Valérie Burdin, Bhushan Borotikar
Date: 2022-02-17 00:00:00
Tasks: Morphological Analysis

Statistical shape models (SSMs) are a popular tool to conduct morphological analysis of anatomical structures which is a crucial step in clinical practices. However, shape representations through SSMs are based on shape coefficients and lack an explicit one-to-one relationship with anatomical measures of clinical relevance. While a shape coefficient embeds a combination of anatomical measures, a formalized approach to find the relationship between them remains elusive in the literature. This limits the use of SSMs to subjective evaluations in clinical practices. We propose a novel SSM controlled by anatomical parameters derived from morphometric analysis. The proposed anatomically parameterized SSM (ANAT-SSM) is based on learning a linear mapping between shape coefficients and selected anatomical parameters. This mapping is learned from a synthetic population generated by the standard SSM. Determining the pseudo-inverse of the mapping allows us to build the ANAT-SSM. We further impose orthogonality constraints to the anatomical parameterization to obtain independent shape variation patterns. The proposed contribution was evaluated on two skeletal databases of femoral and scapular bone shapes using clinically relevant anatomical parameters. Anatomical measures of the synthetically generated shapes exhibited realistic statistics. The learned matrices corroborated well with the obtained statistical relationship, while the two SSMs achieved moderate to excellent performance in predicting anatomical parameters on unseen shapes. This study demonstrates the use of anatomical representation for creating anatomically parameterized SSM and as a result, removes the limited clinical interpretability of standard SSMs. The proposed models could help analyze differences in relevant bone morphometry between populations, and be integrated in patient-specific pre-surgery planning or in-surgery assessment.
Synthetic data for unsupervised polyp segmentation
Authors: Enric Moreu, Kevin McGuinness, Noel E. O'Connor
Date: 2022-02-17 00:00:00
Tasks: 3D geometry, Segmentation

Deep learning has shown excellent performance in analysing medical images. However, datasets are difficult to obtain due privacy issues, standardization problems, and lack of annotations. We address these problems by producing realistic synthetic images using a combination of 3D technologies and generative adversarial networks. We use zero annotations from medical professionals in our pipeline. Our fully unsupervised method achieves promising results on five real polyp segmentation datasets. As a part of this study we release Synth-Colon, an entirely synthetic dataset that includes 20000 realistic colon images and additional details about depth and 3D geometry: https://enric1994.github.io/synth-colon
A General Deep Learning framework for Neuron Instance Segmentation based on Efficient UNet and Morphological Post-processing
Authors: Huaqian Wu, Nicolas Souedet, Caroline Jan, Cédric Clouchoux, Thierry Delzescaux
Date: 2022-02-17 00:00:00
Tasks: Dynamic Reconstruction, Instance Segmentation, Medical Image Analysis, Segmentation, Semantic Segmentation

Recent studies have demonstrated the superiority of deep learning in medical image analysis, especially in cell instance segmentation, a fundamental step for many biological studies. However, the excellent performance of the neural networks requires training on large, unbiased dataset and annotations, which is labor-intensive and expertise-demanding. This paper presents an end-to-end framework to automatically detect and segment NeuN stained neuronal cells on histological images using only point annotations. Unlike traditional nuclei segmentation with point annotation, we propose using point annotation and binary segmentation to synthesize pixel-level annotations. The synthetic masks are used as the ground truth to train the neural network, a U-Net-like architecture with a state-of-the-art network, EfficientNet, as the encoder. Validation results show the superiority of our model compared to other recent methods. In addition, we investigated multiple post-processing schemes and proposed an original strategy to convert the probability map into segmented instances using ultimate erosion and dynamic reconstruction. This approach is easy to configure and outperforms other classical post-processing techniques. This work aims to develop a robust and efficient framework for analyzing neurons using optical microscopic data, which can be used in preclinical biological studies and, more specifically, in the context of neurodegenerative diseases.
Modeling High-Dimensional Data with Unknown Cut Points: A Fusion Penalized Logistic Threshold Regression
Authors: Yinan Lin, Wen Zhou, Zhi Geng, Gexin Xiao, Jianxin Yin
Date: 2022-02-17 00:00:00
Tasks: Model Selection, regression, Variable Selection

In traditional logistic regression models, the link function is often assumed to be linear and continuous in predictors. Here, we consider a threshold model that all continuous features are discretized into ordinal levels, which further determine the binary responses. Both the threshold points and regression coefficients are unknown and to be estimated. For high dimensional data, we propose a fusion penalized logistic threshold regression (FILTER) model, where a fused lasso penalty is employed to control the total variation and shrink the coefficients to zero as a method of variable selection. Under mild conditions on the estimate of unknown threshold points, we establish the non-asymptotic error bound for coefficient estimation and the model selection consistency. With a careful characterization of the error propagation, we have also shown that the tree-based method, such as CART, fulfill the threshold estimation conditions. We find the FILTER model is well suited in the problem of early detection and prediction for chronic disease like diabetes, using physical examination data. The finite sample behavior of our proposed method are also explored and compared with extensive Monte Carlo studies, which supports our theoretical discoveries.
Hybridizing Physical and Data-driven Prediction Methods for Physicochemical Properties
Authors: Fabian Jirasek, Robert Bamler, Stephan Mandt
Date: 2022-02-17 00:00:00
Tasks: Bayesian Inference, BIG-bench Machine Learning

We present a generic way to hybridize physical and data-driven methods for predicting physicochemical properties. The approach `distills' the physical method's predictions into a prior model and combines it with sparse experimental data using Bayesian inference. We apply the new approach to predict activity coefficients at infinite dilution and obtain significant improvements compared to the data-driven and physical baselines and established ensemble methods from the machine learning literature.
Emerging trends in soybean industry
Authors: Siddhartha Paul Tiwari
Date: 2022-02-17 00:00:00
Tasks: Nutrition

Soybean is the most globalized, traded and processed crop commodity. USA, Argentina and Brazil continue to be the top three producers and exporters of soybean and soymeal. Indian soyindustry has also made a mark in the national and global arena. While soymeal, soyoil, lecithin and other soy-derivatives stand to be driven up by commerce, the soyfoods for human health and nutrition need to be further promoted. The changing habitat of commerce in soyderivatives necessitates a shift in strategy, technological tools and policy environment to make Indian soybean industry continue to thrive in the new industrial era. Terms of trade for soyfarming and soy-industry could be further improved. Present trends, volatilities, slowdowns, challenges faced and associated desiderata are accordingly spelt out in the present article.
FOIT: Fast Online Instance Transfer for Improved EEG Emotion Recognition
Authors: Jinpeng Li, Hao Chen, Ting Cai
Date: 2021-02-01 00:00:00
Proceeding: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2021 2
Tasks: Active Learning, Brain Computer Interface, Domain Adaptation, EEG, EEG Emotion Recognition, Electroencephalogram (EEG), Emotion Recognition, Style Transfer, Transfer Learning

The Electroencephalogram (EEG)-based emotion recognition is promising yet limited by the requirement of a large number of training data. Collecting substantial labeled samples in the training trails is the key to the generalization on the test trails. This process is time-consuming and laborious. In recent years, several studies have proposed various semi-supervised learning (e.g., active learning) and transfer learning (e.g., domain adaptation, style transfer mapping) methods to alleviate the requirement on training data. However, most of them are iterative methods, which need considerable training time and are unfeasible in practice. To tackle this problem, we present the Fast Online Instance Transfer (FOIT) for improved affective Brain-computer Interface (aBCI). FOIT selects auxiliary data from historical sessions and (or) other subjects heuristically, which are then combined with the training data for supervised training. The predictions on the test trails are made by the multiclassifier ensemble. As a one-shot algorithm, FOIT avoids time-consuming iterations. Experimental results show that FOIT brings significant improvement in accuracy for the three category classification  (1%-8%) on the SEED dataset and four-category classification (1%-14%) on the SEED-IV dataset in the cross-subject, cross-session, and cross-all scenarios. The time cost over the baselines is moderate (~35s in average for our machine). To achieve the comparative accuracies, the iterative methods require much more time (~45s-~900s). FOIT provides a simple, fast, and practically feasible solution to improve the generalization of aBCIs and allows various choices of classifiers without constrains. Our codes are available online.
EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task
Authors: Riccardo La Grassa, Ignazio Gallo, Nicola Landro
Date: 2021-10-21 00:00:00
Proceeding: CAIP: Computer Analysis of Images and Patterns 2021 10
Tasks: Classification, Fine-Grained Image Classification, Image Classification

Fine-Grained classification models can expressly focus on the relevant details useful to distinguish highly similar classes typically when the intra-class variance is high and the inter-class variance is low given a dataset. Most of these models use part annotations as bounding box, location part, text attributes to enhance the performance of classification and other models use sophisticated techniques to extract an attention map automatically. We assume that part-based approaches as the automatic cropping method suffers from a missing representation of local features, which are fundamental to distinguish similar objects. While Fine-Grained classification endeavours to recognize the leaf of a graph, humans recognize an object trying also to make a semantic association. In this paper, we use the semantic association structured as a hierarchy (taxonomy) as supervised signals and used them in an end-to-end deep neural network model termed as EnGraf-Net. Extensive experiments on three well-known datasets: Cifar-100, CUB-200-2011 and FGVC-Aircraft prove the superiority of EnGraf-Net over many Fine-Grained models and it is competitive with the most recent best models without using any cropping technique or manual annotations.
Bode-based speed Proportional Integral and notch filter tuning of a Permanent Magnet Synchronous Machine driven flexible system
Authors: Santiago Ramos Garces, Abdelmajid Ben Yahya, Nick Van Oosterwyck, Dries Jacques, Stijn Derammelaere
Date: 2022-02-17 00:00:00

A resonance and an antiresonance peak characterize many industrial mechanisms dynamics driven by a Permanent Magnet Synchronous Motor (PMSM). The presence of the resonance peak can lead to vibrations and instability of the system. On that account, advanced methods exist to tune the speed Proportional Integral (PI) controller based on adaptive or fuzzy theory. However, those methods require expertise in control theory and are not available in commercial drives. For that, this paper proposes a Bode-based method for PI parameters selection in combination with a notch filter that can be easily set in any industrial drive. The proposed method is compared with conventional tuning methods in a physical setup.
Learning to Navigate in the Gaussian Mixture Surface
Authors: Riccardo La Grassa, Ignazio Gallo, Nicola Landro
Date: 2021-10-21 00:00:00
Proceeding: CAIP: Computer Analysis of Images and Patterns 2021 10
Tasks: Navigate

In the last years, deep learning models have achieved remarkable generalization capability on computer vision tasks, obtaining excellent results in fine-grained classification problems. Sophisticated approaches based-on discriminative feature learning via patches have been proposed in the literature, boosting the model performances and achieving the state-of-the-art over well-known datasets. Cross-Entropy (CE) loss function is commonly used to enhance the discriminative power of the deep learned features, encouraging the separability between the classes. However, observing the activation map generated by these models in the hidden layer, we realize that many image regions with low discriminative content have a high activation response and this could lead to misclassifications. To address this problem, we propose a loss function called Gaussian Mixture Centers (GMC) loss, leveraging on the idea that data follow multiple unimodal distributions. We aim to reduce variances considering many centers per class, using the information from the hidden layers of a deep model, and decreasing the high response from the unnecessary areas of images detected along the baselines. Using jointly CE and GMC loss, we improve the learning generalization model overcoming the performance of the baselines in several use cases. We show the effectiveness of our approach by carrying out experiments over CUB-200-2011, FGVC-Aircraft, Stanford-Dogs benchmarks, and considering the most recent Convolutional Neural Network (CNN).
Sentinel 2 Time Series Analysis with 3D Feature Pyramid Network and Time Domain Class Activation Intervals for Crop Mapping
Authors: Ignazio Gallo, Riccardo La Grassa, Nicola Landro, Mirco Boschetti
Date: 2021-10-07 00:00:00
Proceeding: isprs: International Journal of Geo-information 2021 10
Tasks: Semantic Segmentation, Time Series, Time Series Analysis, UNET Segmentation

In this paper, we provide an innovative contribution in the research domain dedicated to crop mapping by exploiting the of Sentinel-2 satellite images time series, with the specific aim to extract information on “where and when” crops are grown. The final goal is to set up a workflow able to reliably identify (classify) the different crops that are grown in a given area by exploiting an end-to-end (3+2)D convolutional neural network (CNN) for semantic segmentation. The method also has the ambition to provide information, at pixel level, regarding the period in which a given crop is cultivated during the season. To this end, we propose a solution called Class Activation Interval (CAI) which allows us to interpret, for each pixel, the reasoning made by CNN in the classification determining in which time interval, of the input time series, the class is likely to be present or not. Our experiments, using a public domain dataset, show that the approach is able to accurately detect crop classes with an overall accuracy of about 93% and that the network can detect discriminatory time intervals in which crop is cultivated. These results have twofold importance: (i) demonstrate the ability of the network to correctly interpret the investigated physical process (i.e., bare soil condition, plant growth, senescence and harvesting according to specific cultivated variety) and (ii) provide further information to the end-user (e.g., the presence of crops and its temporal dynamics).
Weakly-Supervised Classification and Detection of Bird Sounds in the Wild.
Authors: Marcos V. Conde, Kumar Shubham, Prateek Agnihotri, Nitin D. Movva, Szilard Bessenyei
Date: 2021-07-10 00:00:00
Proceeding: CLEF 2021 7
Tasks: Audio Classification, Audio Tagging, Bird Audio Detection, Sound Event Detection, Weakly Supervised Classification

It is easier to hear birds than see them, however, they still play an essential role in nature and they are excellent indicators of deteriorating environmental quality and pollution. Recent advances in Machine Learning and Convolutional Neural Networks allow us to detect and classify bird sounds, by doing this, we can assist researchers in monitoring the status and trends of bird populations and biodiversity in ecosystems. We propose a sound detection and classification pipeline for analyzing complex soundscape recordings and identify birdcalls in the background. Our pipeline learns from weak labels, classifies fine-grained bird vocalizations in the wild, and is robust against background sounds (e.g., airplanes, rain, etc). Our solution achieved 10th place of 816 teams at the BirdCLEF 2021 Challenge hosted on Kaggle.
Time Difference of Arrival (TDoA) Localization Combining Weighted Least Squares and Firefly Algorithm
Authors: Peng Wu, Shaojing Su, Zhen Zuo *, Xiaojun Guo, Bei Sun and Xudong Wen
Date: 2019-06-04 00:00:00
Proceeding: MDPI 2019 6

Time difference of arrival (TDoA) based on a group of sensor nodes with known
locations has been widely used to locate targets. Two‐step weighted least squares (TSWLS),
constrained weighted least squares (CWLS), and Newton–Raphson (NR) iteration are commonly
used passive location methods, among which the initial position is needed and the complexity is
high. This paper proposes a hybrid firefly algorithm (hybrid‐FA) method, combining the weighted
least squares (WLS) algorithm and FA, which can reduce computation as well as achieve high
accuracy. The WLS algorithm is performed first, the result of which is used to restrict the search
region for the FA method. Simulations showed that the hybrid‐FA method required far fewer
iterations than the FA method alone to achieve the same accuracy. Additionally, two experiments
were conducted to compare the results of hybrid‐FA with other methods. The findings indicated
that the root‐mean‐square error (RMSE) and mean distance error of the hybrid‐FA method were
lower than that of the NR, TSWLS, and genetic algorithm (GA). On the whole, the hybrid‐FA
outperformed the NR, TSWLS, and GA for TDoA measurement
Physical Layer Authentication for LEO Satellite Constellations
Authors: Ozan Alp Topal, Gunes Karabulut Kurt
Date: 2022-02-17 00:00:00

Physical layer authentication (PLA) is the process of claiming identity of a node based on its physical layer characteristics such as channel fading or hardware imperfections. In this work, we propose a novel PLA method for the inter-satellite communication links (ISLs) of the LEO satellites. In the proposed PLA method, multiple receiving satellites validate the identity of the transmitter by comparing the Doppler frequency measurements with the reference mobility information of the legitimate transmitter and then fuse their decision considering the selected decision rule. Analytical expressions are obtained for the spoofing detection probability and false alarm probability of the fusion methods. Numerically obtained high authentication performance results pave the way to a novel and easily integrable authentication mechanism for the LEO satellite networks.
Multi-Channel Speech Denoising for Machine Ears
Authors: Cong Han, E. Merve Kaya, Kyle Hoefer, Malcolm Slaney, Simon Carlile
Date: 2022-02-17 00:00:00
Tasks: Denoising, Speech Denoising

This work describes a speech denoising system for machine ears that aims to improve speech intelligibility and the overall listening experience in noisy environments. We recorded approximately 100 hours of audio data with reverberation and moderate environmental noise using a pair of microphone arrays placed around each of the two ears and then mixed sound recordings to simulate adverse acoustic scenes. Then, we trained a multi-channel speech denoising network (MCSDN) on the mixture of recordings. To improve the training, we employ an unsupervised method, complex angular central Gaussian mixture model (cACGMM), to acquire cleaner speech from noisy recordings to serve as the learning target. We propose a MCSDN-Beamforming-MCSDN framework in the inference stage. The results of the subjective evaluation show that the cACGMM improves the training data, resulting in better noise reduction and user preference, and the entire system improves the intelligibility and listening experience in noisy situations.
A Two-Stage U-Net for High-Fidelity Denoising of Historical Recordings
Authors: Eloi Moliner, Vesa Välimäki
Date: 2022-02-17 00:00:00
Tasks: Denoising

Enhancing the sound quality of historical music recordings is a long-standing problem. This paper presents a novel denoising method based on a fully-convolutional deep neural network. A two-stage U-Net model architecture is designed to model and suppress the degradations with high fidelity. The method processes the time-frequency representation of audio, and is trained using realistic noisy data to jointly remove hiss, clicks, thumps, and other common additive disturbances from old analog discs. The proposed model outperforms previous methods in both objective and subjective metrics. The results of a formal blind listening test show that real gramophone recordings denoised with this method have significantly better quality than the baseline methods. This study shows the importance of realistic training data and the power of deep learning in audio restoration.