pwc-archive/papers-with-abstracts huggingface dataset:
Uncertainty Estimation for Super-Resolution using ESRGAN
Authors: Maniraj Sai Adapa, Marco Zullich, Matias Valdenegro-Toro
Date: 2024-12-19 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Deep Learning-based image super-resolution (SR) has been gaining traction with the aid of Generative Adversarial Networks. Models like SRGAN and ESRGAN are constantly ranked between the best image SR tools. However, they lack principled ways for estimating predictive uncertainty. In the present work, we enhance these models using Monte Carlo-Dropout and Deep Ensemble, allowing the computation of predictive uncertainty. When coupled with a prediction, uncertainty estimates can provide more information to the model users, highlighting pixels where the SR output might be uncertain, hence potentially inaccurate, if these estimates were to be reliable. Our findings suggest that these uncertainty estimates are decently calibrated and can hence fulfill this goal, while providing no performance drop with respect to the corresponding models without uncertainty estimation.
SolidGS: Consolidating Gaussian Surfel Splatting for Sparse-View Surface Reconstruction
Authors: Zhuowen Shen, YuAn Liu, Zhang Chen, Zhong Li, Jiepeng Wang, Yongqing Liang, Zhengming Yu, Jingdong Zhang, Yi Xu, Scott Schaefer, Xin Li, Wenping Wang
Date: 2024-12-19 00:00:00
Tasks: Novel View Synthesis, Surface Reconstruction

Gaussian splatting has achieved impressive improvements for both novel-view synthesis and surface reconstruction from multi-view images. However, current methods still struggle to reconstruct high-quality surfaces from only sparse view input images using Gaussian splatting. In this paper, we propose a novel method called SolidGS to address this problem. We observed that the reconstructed geometry can be severely inconsistent across multi-views, due to the property of Gaussian function in geometry rendering. This motivates us to consolidate all Gaussians by adopting a more solid kernel function, which effectively improves the surface reconstruction quality. With the additional help of geometrical regularization and monocular normal estimation, our method achieves superior performance on the sparse view surface reconstruction than all the Gaussian splatting methods and neural field methods on the widely used DTU, Tanks-and-Temples, and LLFF datasets.
Leveraging Weak Supervision for Cell Localization in Digital Pathology Using Multitask Learning and Consistency Loss
Authors: Berke Levent Cesur, Ayse Humeyra Dur Karasayar, Pinar Bulutay, Nilgun Kapucuoglu, Cisel Aydin Mericoz, Handan Eren, Omer Faruk Dilbaz, Javidan Osmanli, Burhan Soner Yetkili, Ibrahim Kulac, Can Fahrettin Koyuncu, Cigdem Gunduz-Demir
Date: 2024-12-19 00:00:00
Tasks: Cell Detection

Cell detection and segmentation are integral parts of automated systems in digital pathology. Encoder-decoder networks have emerged as a promising solution for these tasks. However, training of these networks has typically required full boundary annotations of cells, which are labor-intensive and difficult to obtain on a large scale. However, in many applications, such as cell counting, weaker forms of annotations--such as point annotations or approximate cell counts--can provide sufficient supervision for training. This study proposes a new mixed-supervision approach for training multitask networks in digital pathology by incorporating cell counts derived from the eyeballing process--a quick visual estimation method commonly used by pathologists. This study has two main contributions: (1) It proposes a mixed-supervision strategy for digital pathology that utilizes cell counts obtained by eyeballing as an auxiliary supervisory signal to train a multitask network for the first time. (2) This multitask network is designed to concurrently learn the tasks of cell counting and cell localization, and this study introduces a consistency loss that regularizes training by penalizing inconsistencies between the predictions of these two tasks. Our experiments on two datasets of hematoxylin-eosin stained tissue images demonstrate that the proposed approach effectively utilizes the weakest form of annotation, improving performance when stronger annotations are limited. These results highlight the potential of integrating eyeballing-derived ground truths into the network training, reducing the need for resource-intensive annotations.
Maximising Histopathology Segmentation using Minimal Labels via Self-Supervision
Authors: Zeeshan Nisar, Thomas Lampert
Date: 2024-12-19 00:00:00
Tasks: Prognosis, Segmentation

Histopathology, the microscopic examination of tissue samples, is essential for disease diagnosis and prognosis. Accurate segmentation and identification of key regions in histopathology images are crucial for developing automated solutions. However, state-of-art deep learning segmentation methods like UNet require extensive labels, which is both costly and time-consuming, particularly when dealing with multiple stainings. To mitigate this, multi-stain segmentation methods such as MDS1 and UDAGAN have been developed, which reduce the need for labels by requiring only one (source) stain to be labelled. Nonetheless, obtaining source stain labels can still be challenging, and segmentation models fail when they are unavailable. This article shows that through self-supervised pre-training, including SimCLR, BYOL, and a novel approach, HR-CS-CO, the performance of these segmentation methods (UNet, MDS1, and UDAGAN) can be retained even with 95% fewer labels. Notably, with self-supervised pre-training and using only 5% labels, the performance drops are minimal: 5.9% for UNet, 4.5% for MDS1, and 6.2% for UDAGAN, compared to their respective fully supervised counterparts (without pre-training, using 100% labels). The code is available from https://github.com/zeeshannisar/improve_kidney_glomeruli_segmentation [to be made public upon acceptance].
Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation
Authors: Meghana Karri, Amit Soni Arya, Koushik Biswas, Nicol`o Gennaro, Vedat Cicek, Gorkem Durak, Yuri S. Velichko, Ulas Bagci
Date: 2024-12-19 00:00:00
Tasks: Domain Generalization, Image Segmentation, Knowledge Distillation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Semi-supervised Medical Image Segmentation

This work proposes a novel framework, Uncertainty-Guided Cross Attention Ensemble Mean Teacher (UG-CEMT), for achieving state-of-the-art performance in semi-supervised medical image segmentation. UG-CEMT leverages the strengths of co-training and knowledge distillation by combining a Cross-attention Ensemble Mean Teacher framework (CEMT) inspired by Vision Transformers (ViT) with uncertainty-guided consistency regularization and Sharpness-Aware Minimization emphasizing uncertainty. UG-CEMT improves semi-supervised performance while maintaining a consistent network architecture and task setting by fostering high disparity between sub-networks. Experiments demonstrate significant advantages over existing methods like Mean Teacher and Cross-pseudo Supervision in terms of disparity, domain generalization, and medical image segmentation performance. UG-CEMT achieves state-of-the-art results on multi-center prostate MRI and cardiac MRI datasets, where object segmentation is particularly challenging. Our results show that using only 10\% labeled data, UG-CEMT approaches the performance of fully supervised methods, demonstrating its effectiveness in exploiting unlabeled data for robust medical image segmentation. The code is publicly available at \url{https://github.com/Meghnak13/UG-CEMT}
Dataset Augmentation by Mixing Visual Concepts
Authors: Abdullah Al Rahat, Hemanth Venkateswara
Date: 2024-12-19 00:00:00
Tasks: Image Captioning

This paper proposes a dataset augmentation method by fine-tuning pre-trained diffusion models. Generating images using a pre-trained diffusion model with textual conditioning often results in domain discrepancy between real data and generated images. We propose a fine-tuning approach where we adapt the diffusion model by conditioning it with real images and novel text embeddings. We introduce a unique procedure called Mixing Visual Concepts (MVC) where we create novel text embeddings from image captions. The MVC enables us to generate multiple images which are diverse and yet similar to the real data enabling us to perform effective dataset augmentation. We perform comprehensive qualitative and quantitative evaluations with the proposed dataset augmentation approach showcasing both coarse-grained and finegrained changes in generated images. Our approach outperforms state-of-the-art augmentation techniques on benchmark classification tasks.
DCRA-Net: Attention-Enabled Reconstruction Model for Dynamic Fetal Cardiac MRI
Authors: Denis Prokopenko, David F. A. Lloyd, Amedeo Chiribiri, Daniel Rueckert, Joseph V. Hajnal
Date: 2024-12-19 00:00:00
Tasks: Anatomy

Dynamic fetal heart magnetic resonance imaging (MRI) presents unique challenges due to the fast heart rate of the fetus compared to adult subjects and uncontrolled fetal motion. This requires high temporal and spatial resolutions over a large field of view, in order to encompass surrounding maternal anatomy. In this work, we introduce Dynamic Cardiac Reconstruction Attention Network (DCRA-Net) - a novel deep learning model that employs attention mechanisms in spatial and temporal domains and temporal frequency representation of data to reconstruct the dynamics of the fetal heart from highly accelerated free-running (non-gated) MRI acquisitions. DCRA-Net was trained on retrospectively undersampled complex-valued cardiac MRIs from 42 fetal subjects and separately from 153 adult subjects, and evaluated on data from 14 fetal and 39 adult subjects respectively. Its performance was compared to L+S and k-GIN methods in both fetal and adult cases for an undersampling factor of 8x. The proposed network performed better than the comparators for both fetal and adult data, for both regular lattice and centrally weighted random undersampling. Aliased signals due to the undersampling were comprehensively resolved, and both the spatial details of the heart and its temporal dynamics were recovered with high fidelity. The highest performance was achieved when using lattice undersampling, data consistency and temporal frequency representation, yielding PSNR of 38 for fetal and 35 for adult cases. Our method is publicly available at https://github.com/denproc/DCRA-Net.
Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models
Authors: Reza Shirkavand, Peiran Yu, Shangqian Gao, Gowthami Somepalli, Tom Goldstein, Heng Huang
Date: 2024-12-19 00:00:00
Proceeding: CVPR 2025 1
Tasks: Bilevel Optimization, Knowledge Distillation, Style Transfer

Recent advances in diffusion generative models have yielded remarkable progress. While the quality of generated content continues to improve, these models have grown considerably in size and complexity. This increasing computational burden poses significant challenges, particularly in resource-constrained deployment scenarios such as mobile devices. The combination of model pruning and knowledge distillation has emerged as a promising solution to reduce computational demands while preserving generation quality. However, this technique inadvertently propagates undesirable behaviors, including the generation of copyrighted content and unsafe concepts, even when such instances are absent from the fine-tuning dataset. In this paper, we propose a novel bilevel optimization framework for pruned diffusion models that consolidates the fine-tuning and unlearning processes into a unified phase. Our approach maintains the principal advantages of distillation-namely, efficient convergence and style transfer capabilities-while selectively suppressing the generation of unwanted content. This plug-in framework is compatible with various pruning and concept unlearning methods, facilitating efficient, safe deployment of diffusion models in controlled environments.
MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis
Authors: Ho Kei Cheng, Masato Ishii, Akio Hayakawa, Takashi Shibuya, Alexander Schwing, Yuki Mitsufuji
Date: 2024-12-19 00:00:00
Proceeding: CVPR 2025 1
Tasks: Audio Generation, Audio Synthesis, Audio-Visual Synchronization, Video-to-Sound Generation

We propose to synthesize high-quality and synchronized audio, given video and optional text conditions, using a novel multimodal joint training framework MMAudio. In contrast to single-modality training conditioned on (limited) video data only, MMAudio is jointly trained with larger-scale, readily available text-audio data to learn to generate semantically aligned high-quality audio samples. Additionally, we improve audio-visual synchrony with a conditional synchronization module that aligns video conditions with audio latents at the frame level. Trained with a flow matching objective, MMAudio achieves new video-to-audio state-of-the-art among public models in terms of audio quality, semantic alignment, and audio-visual synchronization, while having a low inference time (1.23s to generate an 8s clip) and just 157M parameters. MMAudio also achieves surprisingly competitive performance in text-to-audio generation, showing that joint training does not hinder single-modality performance. Code and demo are available at: https://hkchengrex.github.io/MMAudio
Next Patch Prediction for Autoregressive Visual Generation
Authors: Yatian Pang, Peng Jin, Shuo Yang, Bin Lin, Bin Zhu, Zhenyu Tang, Liuhan Chen, Francis E. H. Tay, Ser-Nam Lim, Harry Yang, Li Yuan
Date: 2024-12-19 00:00:00
Tasks: Image Generation, Prediction

Autoregressive models, built based on the Next Token Prediction (NTP) paradigm, show great potential in developing a unified framework that integrates both language and vision tasks. In this work, we rethink the NTP for autoregressive image generation and propose a novel Next Patch Prediction (NPP) paradigm. Our key idea is to group and aggregate image tokens into patch tokens containing high information density. With patch tokens as a shorter input sequence, the autoregressive model is trained to predict the next patch, thereby significantly reducing the computational cost. We further propose a multi-scale coarse-to-fine patch grouping strategy that exploits the natural hierarchical property of image data. Experiments on a diverse range of models (100M-1.4B parameters) demonstrate that the next patch prediction paradigm could reduce the training cost to around 0.6 times while improving image generation quality by up to 1.0 FID score on the ImageNet benchmark. We highlight that our method retains the original autoregressive model architecture without introducing additional trainable parameters or specifically designing a custom image tokenizer, thus ensuring flexibility and seamless adaptation to various autoregressive models for visual generation.
Multi-concept Model Immunization through Differentiable Model Merging
Authors: Amber Yijia Zheng, Raymond A. Yeh
Date: 2024-12-19 00:00:00
Tasks: model

Model immunization is an emerging direction that aims to mitigate the potential risk of misuse associated with open-sourced models and advancing adaptation methods. The idea is to make the released models' weights difficult to fine-tune on certain harmful applications, hence the name ``immunized''. Recent work on model immunization focuses on the single-concept setting. However, models need to be immunized against multiple concepts in real-world situations. To address this gap, we propose an immunization algorithm that, simultaneously, learns a single ``difficult initialization'' for adaptation methods over a set of concepts. We achieve this by incorporating a differentiable merging layer that combines a set of model weights adapted over multiple concepts. In our experiments, we demonstrate the effectiveness of multi-concept immunization by generalizing prior work's experiment setup of re-learning and personalization adaptation to multiple concepts.
Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration
Authors: Chiu-Han Hsiao, Kai Chen, Tsung-Yu Peng, Wei-Chieh Huang
Date: 2024-12-19 00:00:00
Tasks: Computational Efficiency, Data Integration, Federated Learning

The traditional interpretation of Intravascular Ultrasound (IVUS) images during Percutaneous Coronary Intervention (PCI) is time-intensive and inconsistent, relying heavily on physician expertise. Regulatory restrictions and privacy concerns further hinder data integration across hospital systems, complicating collaborative analysis. To address these challenges, a parallel 2D U-Net model with a multi-stage segmentation architecture has been developed, utilizing federated learning to enable secure data analysis across institutions while preserving privacy. The model segments plaques by identifying and subtracting the External Elastic Membrane (EEM) and lumen areas, with preprocessing converting Cartesian to polar coordinates for improved computational efficiency. Achieving a Dice Similarity Coefficient (DSC) of 0.706, the model effectively identifies plaques and detects circular boundaries in real-time. Collaborative efforts with domain experts enhance plaque burden interpretation through precise quantitative measurements. Future advancements may involve integrating advanced federated learning techniques and expanding datasets to further improve performance and applicability. This adaptable technology holds promise for environments handling sensitive, distributed data, offering potential to optimize outcomes in medical imaging and intervention.
Parametric $ρ$-Norm Scaling Calibration
Authors: Siyuan Zhang, Linbo Xie
Date: 2024-12-19 00:00:00

Output uncertainty indicates whether the probabilistic properties reflect objective characteristics of the model output. Unlike most loss functions and metrics in machine learning, uncertainty pertains to individual samples, but validating it on individual samples is unfeasible. When validated collectively, it cannot fully represent individual sample properties, posing a challenge in calibrating model confidence in a limited data set. Hence, it is crucial to consider confidence calibration characteristics. To counter the adverse effects of the gradual amplification of the classifier output amplitude in supervised learning, we introduce a post-processing parametric calibration method, $\rho$-Norm Scaling, which expands the calibrator expression and mitigates overconfidence due to excessive amplitude while preserving accuracy. Moreover, bin-level objective-based calibrator optimization often results in the loss of significant instance-level information. Therefore, we include probability distribution regularization, which incorporates specific priori information that the instance-level uncertainty distribution after calibration should resemble the distribution before calibration. Experimental results demonstrate the substantial enhancement in the post-processing calibrator for uncertainty calibration with our proposed method.
FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks
Authors: Siddharth Ambekar, Yuhang Yao, Ryan Li, Carlee Joe-Wong
Date: 2024-12-20 00:00:00
Tasks: Graph Attention, Graph Learning, Node Classification, Privacy Preserving

Federated training methods have gained popularity for graph learning with applications including friendship graphs of social media sites and customer-merchant interaction graphs of huge online marketplaces. However, privacy regulations often require locally generated data to be stored on local clients. The graph is then naturally partitioned across clients, with no client permitted access to information stored on another. Cross-client edges arise naturally in such cases and present an interesting challenge to federated training methods, as training a graph model at one client requires feature information of nodes on the other end of cross-client edges. Attempting to retain such edges often incurs significant communication overhead, and dropping them altogether reduces model performance. In simpler models such as Graph Convolutional Networks, this can be fixed by communicating a limited amount of feature information across clients before training, but GATs (Graph Attention Networks) require additional information that cannot be pre-communicated, as it changes from training round to round. We introduce the Federated Graph Attention Network (FedGAT) algorithm for semi-supervised node classification, which approximates the behavior of GATs with provable bounds on the approximation error. FedGAT requires only one pre-training communication round, significantly reducing the communication overhead for federated GAT training. We then analyze the error in the approximation and examine the communication overhead and computational complexity of the algorithm. Experiments show that FedGAT achieves nearly the same accuracy as a GAT model in a centralised setting, and its performance is robust to the number of clients as well as data distribution.
EF-Net: A Deep Learning Approach Combining Word Embeddings and Feature Fusion for Patient Disposition Analysis
Authors: Nafisa Binte Feroz, Chandrima Sarker, Tanzima Ahsan, K M Arefeen Sultan, Raqeebir Rab
Date: 2024-12-20 00:00:00
Tasks: Word Embeddings

One of the most urgent problems is the overcrowding in emergency departments (EDs), caused by an aging population and rising healthcare costs. Patient dispositions have become more complex as a result of the strain on hospital infrastructure and the scarcity of medical resources. Individuals with more dangerous health issues should be prioritized in the emergency room. Thus, our research aims to develop a prediction model for patient disposition using EF-Net. This model will incorporate categorical features into the neural network layer and add numerical features with the embedded categorical features. We combine the EF-Net and XGBoost models to attain higher accuracy in our results. The result is generated using the soft voting technique. In EF-Net, we attained an accuracy of 95.33%, whereas in the Ensemble Model, we achieved an accuracy of 96%. The experiment's analysis shows that EF-Net surpasses existing works in accuracy, AUROC, and F1-Score on the MIMIC-IV-ED dataset, demonstrating its potential as a scalable solution for patient disposition assessment. Our code is available at https://github.com/nafisa67/thesis
Differentially Private Federated Learning of Diffusion Models for Synthetic Tabular Data Generation
Authors: Timur Sattarov, Marco Schreyer, Damian Borth
Date: 2024-12-20 00:00:00
Tasks: Denoising, Federated Learning, Privacy Preserving, Synthetic Data Generation, Tabular Data Generation

The increasing demand for privacy-preserving data analytics in finance necessitates solutions for synthetic data generation that rigorously uphold privacy standards. We introduce DP-Fed-FinDiff framework, a novel integration of Differential Privacy, Federated Learning and Denoising Diffusion Probabilistic Models designed to generate high-fidelity synthetic tabular data. This framework ensures compliance with stringent privacy regulations while maintaining data utility. We demonstrate the effectiveness of DP-Fed-FinDiff on multiple real-world financial datasets, achieving significant improvements in privacy guarantees without compromising data quality. Our empirical evaluations reveal the optimal trade-offs between privacy budgets, client configurations, and federated optimization strategies. The results affirm the potential of DP-Fed-FinDiff to enable secure data sharing and robust analytics in highly regulated domains, paving the way for further advances in federated learning and privacy-preserving data synthesis.