pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Tier Tournaments: Matching and Scoring Players
Authors: Steven J. Brams, Mehmet S. Ismail
Date: 2024-07-18 00:00:00

We introduce a novel system of matching and scoring players in tournaments, called Multi-Tier Tournaments, illustrated by chess and based on the following rules: 1. Players are divided into skill-based tiers, based on their Elo ratings. 2. Starting with one or more mini-tournaments of the least skilled players (Tier 1), the winner or winners -- after playing multiple opponents -- move to the next-higher tier. 3. The winners progress to a final tier of the best-performing players from lower tiers as well as players with the highest Elo ratings. 4. Performance in each tier is given by a player's Tournament Score (TS), which depends on his/her wins, losses, and draws (not on his/her Elo rating). Whereas a player's Elo rating determines in which mini-tournament he/she starts play, TS and its associated tie-breaking rules determine whether a player moves up to higher tiers and, in the final mini-tournament, wins the tournament. This combination of players' past Elo ratings and current TS's provides a fair and accurate measure of a player's standing among the players in the tournament. We apply a variation of Multi-Tier Tournaments to the top 20 active chess players in the world (as of February 2024). Using a dataset of 1209 head-to-head games, we illustrate the viability of giving lower-rated players the opportunity to progress and challenge higher-rated players. We also briefly discuss the application of Multi-Tier Tournaments to baseball, soccer, and other sports that emphasize physical rather than mental skills.
Robustness of Speech Separation Models for Similar-pitch Speakers
Authors: Bunlong Lay, Sebastian Zaczek, Kristina Tesch, Timo Gerkmann
Date: 2024-07-22 00:00:00
Tasks: speech-recognition, Speech Recognition, Speech Separation

Single-channel speech separation is a crucial task for enhancing speech recognition systems in multi-speaker environments. This paper investigates the robustness of state-of-the-art Neural Network models in scenarios where the pitch differences between speakers are minimal. Building on earlier findings by Ditter and Gerkmann, which identified a significant performance drop for the 2018 Chimera++ under similar-pitch conditions, our study extends the analysis to more recent and sophisticated Neural Network models. Our experiments reveal that modern models have substantially reduced the performance gap for matched training and testing conditions. However, a substantial performance gap persists under mismatched conditions, with models performing well for large pitch differences but showing worse performance if the speakers' pitches are similar. These findings motivate further research into the generalizability of speech separation models to similar-pitch speakers and unseen data.
Multi-label audio classification with a noisy zero-shot teacher
Authors: Sebastian Braun, Hannes Gamper
Date: 2024-07-20 00:00:00
Tasks: Audio Classification, Data Augmentation

We propose a novel training scheme using self-label correction and data augmentation methods designed to deal with noisy labels and improve real-world accuracy on a polyphonic audio content detection task. The augmentation method reduces label noise by mixing multiple audio clips and joining their labels, while being compatible with multiple active labels. We additionally show that performance can be improved by a self-label correction method using the same pretrained model. Finally, we show that it is feasible to use a strong zero-shot model such as CLAP to generate labels for unlabeled data and improve the results using the proposed training and label enhancement methods. The resulting model performs similar to CLAP while being an efficient mobile device friendly architecture and can be quickly adapted to unlabeled sound classes.
Wideband Relative Transfer Function (RTF) Estimation Exploiting Frequency Correlations
Authors: Giovanni Bologni, Richard C. Hendriks, Richard Heusdens
Date: 2024-07-19 00:00:00
Tasks: Fault Detection, Speech Enhancement

This article focuses on estimating relative transfer functions (RTFs) for beamforming applications. Traditional methods often assume that spectra are uncorrelated, an assumption that is often violated in practical scenarios due to factors such as time-domain windowing or the non-stationary nature of signals, as observed in speech. To overcome these limitations, we propose an RTF estimation technique that leverages spectral and spatial correlations through subspace analysis. Additionally, we derive Cram\'er--Rao bounds (CRBs) for the RTF estimation task, providing theoretical insights into the achievable estimation accuracy. These bounds reveal that channel estimation can be performed more accurately if the noise or the target signal exhibits spectral correlations. Experiments with both real and synthetic data show that our technique outperforms the narrowband maximum-likelihood estimator, known as covariance whitening (CW), when the target exhibits spectral correlations. Although the proposed algorithm generally achieves accuracy close to the theoretical bound, there is potential for further improvement, especially in scenarios with highly spectrally correlated noise. While channel estimation has various applications, we demonstrate the method using a minimum variance distortionless (MVDR) beamformer for multichannel speech enhancement. A free Python implementation is also provided.
Improving the Robustness and Clinical Applicability of Automatic Respiratory Sound Classification Using Deep Learning-Based Audio Enhancement: Algorithm Development and Validation
Authors: Jing-Tong Tzeng, Jeng-Lin Li, Huan-Yu Chen, Chun-Hsiang Huang, Chi-Hsin Chen, Cheng-Yi Fan, Edward Pei-Chuan Huang, Chi-Chun Lee
Date: 2024-07-18 00:00:00
Tasks: Classification, Data Augmentation, Diagnostic, Sound Classification

Deep learning techniques have shown promising results in the automatic classification of respiratory sounds. However, accurately distinguishing these sounds in real-world noisy conditions remains challenging for clinical deployment. In addition, predicting signals with only background noise may reduce user trust in the system. This study explores the feasibility and effectiveness of incorporating a deep learning-based audio enhancement step into automatic respiratory sound classification systems to improve robustness and clinical applicability. We conducted extensive experiments using various audio enhancement model architectures, including time-domain and time-frequency-domain approaches, combined with multiple classification models to evaluate the module's effectiveness. The classification performance was compared against the noise injection data augmentation method. These experiments were carried out on two datasets: the ICBHI respiratory sound dataset and the FABS dataset. Furthermore, a physician validation study assessed the system's clinical utility. Integrating the audio enhancement module resulted in a 21.9% increase in the ICBHI classification score and a 4.1% improvement on the FABS dataset in multi-class noisy scenarios. Quantitative analysis revealed efficiency gains, higher diagnostic confidence, and increased trust, with workflows using enhanced audio improving diagnostic sensitivity by 11.6% and enabling high-confidence diagnoses. Incorporating an audio enhancement algorithm boosts the robustness and clinical utility of automatic respiratory sound classification systems, enhancing performance in noisy environments and fostering greater trust among medical professionals.
Semi-Supervised Contrastive Learning of Musical Representations
Authors: Julien Guinot, Elio Quinton, György Fazekas
Date: 2024-07-18 00:00:00
Tasks: Contrastive Learning, Information Retrieval, Music Information Retrieval, Transfer Learning

Despite the success of contrastive learning in Music Information Retrieval, the inherent ambiguity of contrastive self-supervision presents a challenge. Relying solely on augmentation chains and self-supervised positive sampling strategies can lead to a pretraining objective that does not capture key musical information for downstream tasks. We introduce semi-supervised contrastive learning (SemiSupCon), a simple method for leveraging musically informed labeled data (supervision signals) in the contrastive learning of musical representations. Our approach introduces musically relevant supervision signals into self-supervised contrastive learning by combining supervised and self-supervised contrastive objectives in a simpler framework than previous approaches. This framework improves downstream performance and robustness to audio corruptions on a range of downstream MIR tasks with moderate amounts of labeled data. Our approach enables shaping the learned similarity metric through the choice of labeled data that (1) infuses the representations with musical domain knowledge and (2) improves out-of-domain performance with minimal general downstream performance loss. We show strong transfer learning performance on musically related yet not trivially similar tasks - such as pitch and key estimation. Additionally, our approach shows performance improvement on automatic tagging over self-supervised approaches with only 5\% of available labels included in pretraining.
Enhancing Cell Instance Segmentation in Scanning Electron Microscopy Images via a Deep Contour Closing Operator
Authors: Florian Robert, Alexia Calovoulos, Laurent Facq, Fanny Decoeur, Etienne Gontier, Christophe F. Grosset, Baudouin Denis de Senneville
Date: 2024-07-22 00:00:00
Tasks: Cell Segmentation, Instance Segmentation, Semantic Segmentation

Accurately segmenting and individualizing cells in SEM images is a highly promising technique for elucidating tissue architecture in oncology. While current AI-based methods are effective, errors persist, necessitating time-consuming manual corrections, particularly in areas where the quality of cell contours in the image is poor and requires gap filling. This study presents a novel AI-driven approach for refining cell boundary delineation to improve instance-based cell segmentation in SEM images, also reducing the necessity for residual manual correction. A CNN COp-Net is introduced to address gaps in cell contours, effectively filling in regions with deficient or absent information. The network takes as input cell contour probability maps with potentially inadequate or missing information and outputs corrected cell contour delineations. The lack of training data was addressed by generating low integrity probability maps using a tailored PDE. We showcase the efficacy of our approach in augmenting cell boundary precision using both private SEM images from PDX hepatoblastoma tissues and publicly accessible images datasets. The proposed cell contour closing operator exhibits a notable improvement in tested datasets, achieving respectively close to 50% (private data) and 10% (public data) increase in the accurately-delineated cell proportion compared to state-of-the-art methods. Additionally, the need for manual corrections was significantly reduced, therefore facilitating the overall digitalization process. Our results demonstrate a notable enhancement in the accuracy of cell instance segmentation, particularly in highly challenging regions where image quality compromises the integrity of cell boundaries, necessitating gap filling. Therefore, our work should ultimately facilitate the study of tumour tissue bioarchitecture in onconanotomy field.
Adaptive Extensions of Unbiased Risk Estimators for Unsupervised Magnetic Resonance Image Denoising
Authors: Reeshad Khan, Dr. John Gauch, Dr. Ukash Nakarmi
Date: 2024-07-22 00:00:00
Tasks: Denoising, Image Denoising

The application of Deep Neural Networks (DNNs) to image denoising has notably challenged traditional denoising methods, particularly within complex noise scenarios prevalent in medical imaging. Despite the effectiveness of traditional and some DNN-based methods, their reliance on high-quality, noiseless ground truth images limits their practical utility. In response to this, our work introduces and benchmarks innovative unsupervised learning strategies, notably Stein's Unbiased Risk Estimator (SURE), its extension (eSURE), and our novel implementation, the Extended Poisson Unbiased Risk Estimator (ePURE), within medical imaging frameworks. This paper presents a comprehensive evaluation of these methods on MRI data afflicted with Gaussian and Poisson noise types, a scenario typical in medical imaging but challenging for most denoising algorithms. Our main contribution lies in the effective adaptation and implementation of the SURE, eSURE, and particularly the ePURE frameworks for medical images, showcasing their robustness and efficacy in environments where traditional noiseless ground truth cannot be obtained.
SAM2CLIP2SAM: Vision Language Model for Segmentation of 3D CT Scans for Covid-19 Detection
Authors: Dimitrios Kollias, Anastasios Arsenos, James Wingate, Stefanos Kollias
Date: 2024-07-22 00:00:00
Tasks: Language Modeling, Language Modelling, Segmentation

This paper presents a new approach for effective segmentation of images that can be integrated into any model and methodology; the paradigm that we choose is classification of medical images (3-D chest CT scans) for Covid-19 detection. Our approach includes a combination of vision-language models that segment the CT scans, which are then fed to a deep neural architecture, named RACNet, for Covid-19 detection. In particular, a novel framework, named SAM2CLIP2SAM, is introduced for segmentation that leverages the strengths of both Segment Anything Model (SAM) and Contrastive Language-Image Pre-Training (CLIP) to accurately segment the right and left lungs in CT scans, subsequently feeding these segmented outputs into RACNet for classification of COVID-19 and non-COVID-19 cases. At first, SAM produces multiple part-based segmentation masks for each slice in the CT scan; then CLIP selects only the masks that are associated with the regions of interest (ROIs), i.e., the right and left lungs; finally SAM is given these ROIs as prompts and generates the final segmentation mask for the lungs. Experiments are presented across two Covid-19 annotated databases which illustrate the improved performance obtained when our method has been used for segmentation of the CT scans.
Predicting the Best of N Visual Trackers
Authors: Basit Alawode, Sajid Javed, Arif Mahmood, Jiri Matas
Date: 2024-07-22 00:00:00
Tasks: Self-Supervised Learning

We observe that the performance of SOTA visual trackers surprisingly strongly varies across different video attributes and datasets. No single tracker remains the best performer across all tracking attributes and datasets. To bridge this gap, for a given video sequence, we predict the "Best of the N Trackers", called the BofN meta-tracker. At its core, a Tracking Performance Prediction Network (TP2N) selects a predicted best performing visual tracker for the given video sequence using only a few initial frames. We also introduce a frame-level BofN meta-tracker which keeps predicting best performer after regular temporal intervals. The TP2N is based on self-supervised learning architectures MocoV2, SwAv, BT, and DINO; experiments show that the DINO with ViT-S as a backbone performs the best. The video-level BofN meta-tracker outperforms, by a large margin, existing SOTA trackers on nine standard benchmarks - LaSOT, TrackingNet, GOT-10K, VOT2019, VOT2021, VOT2022, UAV123, OTB100, and WebUAV-3M. Further improvement is achieved by the frame-level BofN meta-tracker effectively handling variations in the tracking scenarios within long sequences. For instance, on GOT-10k, BofN meta-tracker average overlap is 88.7% and 91.1% with video and frame-level settings respectively. The best performing tracker, RTS, achieves 85.20% AO. On VOT2022, BofN expected average overlap is 67.88% and 70.98% with video and frame level settings, compared to the best performing ARTrack, 64.12%. This work also presents an extensive evaluation of competitive tracking methods on all commonly used benchmarks, following their protocols. The code, the trained models, and the results will soon be made publicly available on https://github.com/BasitAlawode/Best_of_N_Trackers.
A Diffusion Model for Simulation Ready Coronary Anatomy with Morpho-skeletal Control
Authors: Karim Kadry, Shreya Gupta, Jonas Sogbadji, Michiel Schaap, Kersten Petersen, Takuya Mizukami, Carlos Collet, Farhad R. Nezami, Elazer R. Edelman
Date: 2024-07-22 00:00:00
Tasks: Anatomy, counterfactual, Counterfactual Reasoning

Virtual interventions enable the physics-based simulation of device deployment within coronary arteries. This framework allows for counterfactual reasoning by deploying the same device in different arterial anatomies. However, current methods to create such counterfactual arteries face a trade-off between controllability and realism. In this study, we investigate how Latent Diffusion Models (LDMs) can custom synthesize coronary anatomy for virtual intervention studies based on mid-level anatomic constraints such as topological validity, local morphological shape, and global skeletal structure. We also extend diffusion model guidance strategies to the context of morpho-skeletal conditioning and propose a novel guidance method for continuous attributes that adaptively updates the negative guiding condition throughout sampling. Our framework enables the generation and editing of coronary anatomy in a controllable manner, allowing device designers to derive mechanistic insights regarding anatomic variation and simulated device deployment.
Subthalamic Nucleus segmentation in high-field Magnetic Resonance data. Is space normalization by template co-registration necessary?
Authors: Tomás Lima, Igor Varga, Eduard Bakštein, Daniel Novák, Victor Alves
Date: 2024-07-22 00:00:00
Tasks: Segmentation

Deep Brain Stimulation (DBS) is one of the most successful methods to diminish late-stage Parkinson's Disease (PD) symptoms. It is a delicate surgical procedure which requires detailed pre-surgical patient's study. High-field Magnetic Resonance Imaging (MRI) has proven its improved capacity of capturing the Subthalamic Nucleus (STN) - the main target of DBS in PD - in greater detail than lower field images. Here, we present a comparison between the performance of two different Deep Learning (DL) automatic segmentation architectures, one based in the registration to a brain template and the other performing the segmentation in in the MRI acquisition native space. The study was based on publicly available high-field 7 Tesla (T) brain MRI datasets of T1-weighted and T2-weighted sequences. nnUNet was used on the segmentation step of both architectures, while the data pre and post-processing pipelines diverged. The evaluation metrics showed that the performance of the segmentation directly in the native space yielded better results for the STN segmentation, despite not showing any advantage over the template-based method for the to other analysed structures: the Red Nucleus (RN) and the Substantia Nigra (SN).
Spatial-Division Augmented Occupancy Field for Bone Shape Reconstruction from Biplanar X-Rays
Authors: Jixiang Chen, Yiqun Lin, Haoran Sun, Xiaomeng Li
Date: 2024-07-22 00:00:00
Tasks: Anatomy, Surface Reconstruction, Value prediction

Retrieving 3D bone anatomy from biplanar X-ray images is crucial since it can significantly reduce radiation exposure compared to traditional CT-based methods. Although various deep learning models have been proposed to address this complex task, they suffer from two limitations: 1) They employ voxel representation for bone shape and exploit 3D convolutional layers to capture anatomy prior, which are memory-intensive and limit the reconstruction resolution. 2) They overlook the prevalent occlusion effect within X-ray images and directly extract features using a simple loss, which struggles to fully exploit complex X-ray information. To tackle these concerns, we present Spatial-division Augmented Occupancy Field~(SdAOF). SdAOF adopts the continuous occupancy field for shape representation, reformulating the reconstruction problem as a per-point occupancy value prediction task. Its implicit and continuous nature enables memory-efficient training and fine-scale surface reconstruction at different resolutions during the inference. Moreover, we propose a novel spatial-division augmented distillation strategy to provide feature-level guidance for capturing the occlusion relationship. Extensive experiments on the pelvis reconstruction dataset show that SdAOF outperforms state-of-the-art methods and reconstructs fine-scale bone surfaces.The code is available at https://github.com/xmed-lab/SdAOF
Iterative approach to reconstructing neural disparity fields from light-field data
Authors: Ligen Shi, Chang Liu, Xing Zhao, Jun Qiu
Date: 2024-07-22 00:00:00

This study proposes a neural disparity field (NDF) that establishes an implicit, continuous representation of scene disparity based on a neural field and an iterative approach to address the inverse problem of NDF reconstruction from light-field data. NDF enables seamless and precise characterization of disparity variations in three-dimensional scenes and can discretize disparity at any arbitrary resolution, overcoming the limitations of traditional disparity maps that are prone to sampling errors and interpolation inaccuracies. The proposed NDF network architecture utilizes hash encoding combined with multilayer perceptrons to capture detailed disparities in texture levels, thereby enhancing its ability to represent the geometric information of complex scenes. By leveraging the spatial-angular consistency inherent in light-field data, a differentiable forward model to generate a central view image from the light-field data is developed. Based on the forward model, an optimization scheme for the inverse problem of NDF reconstruction using differentiable propagation operators is established. Furthermore, an iterative solution method is adopted to reconstruct the NDF in the optimization scheme, which does not require training datasets and applies to light-field data captured by various acquisition methods. Experimental results demonstrate that high-quality NDF can be reconstructed from light-field data using the proposed method. High-resolution disparity can be effectively recovered by NDF, demonstrating its capability for the implicit, continuous representation of scene disparities.
PRIME: Blind Multispectral Unmixing Using Virtual Quantum Prism and Convex Geometry
Authors: Chia-Hsiang Lin, Jhao-Ting Lin
Date: 2024-07-22 00:00:00
Tasks: blind source separation

Multispectral unmixing (MU) is critical due to the inevitable mixed pixel phenomenon caused by the limited spatial resolution of typical multispectral images in remote sensing. However, MU mathematically corresponds to the underdetermined blind source separation problem, thus highly challenging, preventing researchers from tackling it. Previous MU works all ignore the underdetermined issue, and merely consider scenarios with more bands than sources. This work attempts to resolve the underdetermined issue by further conducting the light-splitting task using a network-inspired virtual prism, and as this task is challenging, we achieve so by incorporating the very advanced quantum feature extraction techniques. We emphasize that the prism is virtual (allowing us to fix the spectral response as a simple deterministic matrix), so the virtual hyperspectral image (HSI) it generates has no need to correspond to some real hyperspectral sensor; in other words, it is good enough as long as the virtual HSI satisfies some fundamental properties of light splitting (e.g., non-negativity and continuity). With the above virtual quantum prism, we know that the virtual HSI is expected to possess some desired simplex structure. This allows us to adopt the convex geometry to unmix the spectra, followed by downsampling the pure spectra back to the multispectral domain, thereby achieving MU. Experimental evidence shows great potential of our MU algorithm, termed as prism-inspired multispectral endmember extraction (PRIME).