Tootfinder

nice quote from Vivek Murthy
(interviewed by Eric Topol)
"... a triad of success ... a triad of fulfillment.
"... how does society define success for you? And they would tell me some version of money, fame, and power. If you had all three of those, then you really hit the lottery and people will write books about you, make documentaries about you, you'll have made it. But ... The triad to fulfillment is rooted in relationships, service, and purpose"
#life #success

Delirium often goes underreported, making it hard for #AI to predict accurately. Our review of 120 studies found common data issues that hurt model reliability. We created a step-by-step guide to help build fairer, more accurate models—so we eventually can detect #delirium earlier and im…

Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality
Naihe Feng, Yi Sui, Shiyi Hou, Jesse C. Cresswell, Ga Wu
https://arxiv.org/abs/2506.20978

Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality
Existing research on Retrieval-Augmented Generation (RAG) primarily focuses on improving overall question-answering accuracy, often overlooking the quality of sub-claims within generated responses. Recent methods that attempt to improve RAG trustworthiness, such as through auto-evaluation metrics, lack probabilistic guarantees or require ground truth answers. To address these limitations, we propose Conformal-RAG, a novel framework inspired by recent applications of conformal prediction (CP) on…

Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems
Benedetta Muscato, Lucia Passaro, Gizem Gezici, Fosca Giannotti
https://arxiv.org/abs/2506.20209 …

Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems
In the realm of Natural Language Processing (NLP), common approaches for handling human disagreement consist of aggregating annotators' viewpoints to establish a single ground truth. However, prior studies show that disregarding individual opinions can lead can lead to the side effect of underrepresenting minority perspectives, especially in subjective tasks, where annotators may systematically disagree because of their preferences. Recognizing that labels reflect the diverse backgrounds, life …

Evaluating Disassembly Errors With Only Binaries
Lambang Akbar Wijayadi, Yuancheng Jiang, Roland H. C. Yap, Zhenkai Liang, Zhuohao Liu
https://arxiv.org/abs/2506.20109

Evaluating Disassembly Errors With Only Binaries
Disassemblers are crucial in the analysis and modification of binaries. Existing works showing disassembler errors largely rely on practical implementation without specific guarantees and assume source code and compiler toolchains to evaluate ground truth. However, the assumption of source code is contrary to typical binary scenarios where only the binary is available. In this work, we investigate an approach with minimal assumptions and a sound approach to disassembly error evaluation that doe…

A Computationally Aware Multi Objective Framework for Camera LiDAR Calibration
Venkat Karramreddy, Rangarajan Ramanujam
https://arxiv.org/abs/2506.20636 ht…

A Computationally Aware Multi Objective Framework for Camera LiDAR Calibration
Accurate extrinsic calibration between LiDAR and camera sensors is important for reliable perception in autonomous systems. In this paper, we present a novel multi-objective optimization framework that jointly minimizes the geometric alignment error and computational cost associated with camera-LiDAR calibration. We optimize two objectives: (1) error between projected LiDAR points and ground-truth image edges, and (2) a composite metric for computational cost reflecting runtime and resource usa…

Active View Selector: Fast and Accurate Active View Selection with Cross Reference Image Quality Assessment
Zirui Wang, Yash Bhalgat, Ruining Li, Victor Adrian Prisacariu
https://arxiv.org/abs/2506.19844

Active View Selector: Fast and Accurate Active View Selection with Cross Reference Image Quality Assessment
We tackle active view selection in novel view synthesis and 3D reconstruction. Existing methods like FisheRF and ActiveNeRF select the next best view by minimizing uncertainty or maximizing information gain in 3D, but they require specialized designs for different 3D representations and involve complex modelling in 3D space. Instead, we reframe this as a 2D image quality assessment (IQA) task, selecting views where current renderings have the lowest quality. Since ground-truth images for candid…

Flexible MOF Generation with Torsion-Aware Flow Matching
Nayoung Kim, Seongsu Kim, Sungsoo Ahn
https://arxiv.org/abs/2505.17914 https://

Flexible MOF Generation with Torsion-Aware Flow Matching
Designing metal-organic frameworks (MOFs) with novel chemistries is a long-standing challenge due to their large combinatorial space and the complex 3D arrangements of building blocks. While recent deep generative models have enabled scalable MOF generation, they assume (1) a fixed set of building blocks and (2) known ground-truth local block-wise 3D coordinates. However, this limits their ability to (1) design novel MOFs and (2) generate the structure using novel building blocks. We propose a …

A Deep Learning Based Method for Fast Registration of Cardiac Magnetic Resonance Images
Benjamin Graham
https://arxiv.org/abs/2506.19167 https://

A Deep Learning Based Method for Fast Registration of Cardiac Magnetic Resonance Images
Image registration is used in many medical image analysis applications, such as tracking the motion of tissue in cardiac images, where cardiac kinematics can be an indicator of tissue health. Registration is a challenging problem for deep learning algorithms because ground truth transformations are not feasible to create, and because there are potentially multiple transformations that can produce images that appear correlated with the goal. Unsupervised methods have been proposed to learn to pr…

Beyond Syntax: Action Semantics Learning for App Agents
Bohan Tang, Dezhao Luo, Jingxuan Chen, Shaogang Gong, Jianye Hao, Jun Wang, Kun Shao
https://arxiv.org/abs/2506.17697

Beyond Syntax: Action Semantics Learning for App Agents
The advent of Large Language Models (LLMs) enables the rise of App agents that interpret user intent and operate smartphone Apps through actions such as clicking and scrolling. While prompt-based solutions with closed LLM APIs show promising ability, they incur heavy compute costs and external API dependency. Fine-tuning smaller open-source LLMs solves these limitations. However, current fine-tuning methods use a syntax learning paradigm that forces agents to reproduce exactly the ground truth …

Machine Learning-based Context-Aware EMAs: An Offline Feasibility Study
Zachary D King, Maryam Khalid, Han Yu, Kei Shibuya, Khadija Zanna, Marzieh Majd, Ryan L Brown, Yufei Shen, Thomas Vaessen, George Kypriotakis, Christopher P Fagundes, Akane Sano
https://arxiv.org/abs/2506.15834

Machine Learning-based Context-Aware EMAs: An Offline Feasibility Study
Mobile health (mHealth) systems help researchers monitor and care for patients in real-world settings. Studies utilizing mHealth applications use Ecological Momentary Assessment (EMAs), passive sensing, and contextual features to develop emotion recognition models, which rely on EMA responses as ground truth. Due to this, it is crucial to consider EMA compliance when conducting a successful mHealth study. Utilizing machine learning is one approach that can solve this problem by sending EMAs bas…

TrainVerify: Equivalence-Based Verification for Distributed LLM Training
Yunchi Lu, Youshan Miao, Cheng Tan, Peng Huang, Yi Zhu, Xian Zhang, Fan Yang
https://arxiv.org/abs/2506.15961

TrainVerify: Equivalence-Based Verification for Distributed LLM Training
Training large language models (LLMs) at scale requires parallel execution across thousands of devices, incurring enormous computational costs. Yet, these costly distributed trainings are rarely verified, leaving them prone to silent errors and potentially wasting millions of GPU hours. We introduce TrainVerify, a system for verifiable distributed training of LLMs. Given a deep learning model's logical specification as the ground truth, TrainVerify formally verifies that a distributed parallel …

US political contradictions; knowledge systems
As Trump at least partially succeeds in constructing an alternate reality for his most ardent followers, it's tempting to think of his dogma as false, in contrast to some imagined "truth" which his non-followers are smart enough to believe in. But a more nuanced view of knowledge would admit that different groups of people have different shared truths, constituting different knowledge systems which each deviate from what's objectively measurable in different ways, and in fact they each accept different standards of what is objective, so there's not really a single "ground truth" we can even compare to to determine which of these knowledge systems is "more correct" (similar problems arise even if we only care about "more useful").
To make this more concrete, we can see that e.g., competing quantum physics theories, or likewise competing religious beliefs, have no reasonable basis on which to judge between them, either in terms of "truth" or "utility." So the Trump-dogma knowledge system, although bad, morally repugnant, etc., can't so easily be dismissed as "false" in my view. "Distorted" or "malignant" or "evil" or "contradictory" are better monikers, in my opinion.
But what I'm even more interested in thinking about is: in what ways does the current American liberal "common sense" knowledge system already bear the scars of past fascist lies & contradictions? I can think of a few:
"Columbus was an explorer."
This is "factually accurate" in the same way some of Trump's propaganda is, but it's also a cruel distortion of "Columbus was a child murderer," and it's a misrepresentation that serves an evil purpose, yet which is widely taught in elementary schools today.
Another: "dropping atomic bombs on civilians in Japan was necessary to end WWII."
Perhaps in the future we'll have "family separation & the 2025 ICE crackdowns were necessary to end the immigration crisis," although I dearly hope not.
"Reparations for slavery aren't reasonable," is yet another...
I'll close this rambling with a question: what other fascist lies have you noticed that are normalized in America right now from past Trump-like leaders (or even from less overtly fascist institutions)?

Uncovering Reliable Indicators: Improving IoC Extraction from Threat Reports
Evangelos Froudakis, Athanasios Avgetidis, Sean Tyler Frankum, Roberto Perdisci, Manos Antonakakis, Angelos Keromytis
https://arxiv.org/abs/2506.11325

Uncovering Reliable Indicators: Improving IoC Extraction from Threat Reports
Indicators of Compromise (IoCs) are critical for threat detection and response, marking malicious activity across networks and systems. Yet, the effectiveness of automated IoC extraction systems is fundamentally limited by one key issue: the lack of high-quality ground truth. Current extraction tools rely either on manually extracted ground truth, which is labor-intensive and costly, or on automated ground truth creation methods that include non-malicious artifacts, leading to inflated false po…

orGAN: A Synthetic Data Augmentation Pipeline for Simultaneous Generation of Surgical Images and Ground Truth Labels
Niran Nataraj, Maina Sogabe, Kenji Kawashima
https://arxiv.org/abs/2506.14303

orGAN: A Synthetic Data Augmentation Pipeline for Simultaneous Generation of Surgical Images and Ground Truth Labels
Deep learning in medical imaging faces obstacles: limited data diversity, ethical issues, high acquisition costs, and the need for precise annotations. Bleeding detection and localization during surgery is especially challenging due to the scarcity of high-quality datasets that reflect real surgical scenarios. We propose orGAN, a GAN-based system for generating high-fidelity, annotated surgical images of bleeding. By leveraging small "mimicking organ" datasets, synthetic models that replicate t…

On the existence of consistent adversarial attacks in high-dimensional linear classification
Matteo Vilucchio, Lenka Zdeborov\'a, Bruno Loureiro
https://arxiv.org/abs/2506.12454

On the existence of consistent adversarial attacks in high-dimensional linear classification
What fundamentally distinguishes an adversarial attack from a misclassification due to limited model expressivity or finite data? In this work, we investigate this question in the setting of high-dimensional binary classification, where statistical effects due to limited data availability play a central role. We introduce a new error metric that precisely capture this distinction, quantifying model vulnerability to consistent adversarial attacks -- perturbations that preserve the ground-truth l…

Posterior contraction rates of computational methods for Bayesian data assimilation
Erik Burman, Mingfei Lu
https://arxiv.org/abs/2506.14685 https://

Posterior contraction rates of computational methods for Bayesian data assimilation
In this paper, we analyze posterior consistency of a Bayesian data assimilation problem under discretization. We prove convergence rates for the discrete posterior to ground truth solution under both conforming discretization and finite element discretization (usually non-conforming). The analysis is based on the coupling of asymptotics between the number of samples and the dimension of discrete spaces. In the finite element discretization, tailor-made discrete priors, instead of the discretiza…

This https://arxiv.org/abs/2505.22772 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Calibrated Value-Aware Model Learning with Stochastic Environment Models
The idea of value-aware model learning, that models should produce accurate value estimates, has gained prominence in model-based reinforcement learning. The MuZero loss, which penalizes a model's value function prediction compared to the ground-truth value function, has been utilized in several prominent empirical works in the literature. However, theoretical investigation into its strengths and weaknesses is limited. In this paper, we analyze the family of value-aware model learning losses, w…

Deep learning inference with the Event Horizon Telescope II. The Zingularity framework for Bayesian artificial neural networks
M. Janssen, C. -k. Chan, J. Davelaar, M. Wielgus
https://arxiv.org/abs/2506.13875

Deep learning inference with the Event Horizon Telescope II. The Zingularity framework for Bayesian artificial neural networks
(abridged) In this second paper in our publication series, we present the open-source Zingularity framework for parameter inference with deep Bayesian artificial neural networks. We carried out out supervised learning with synthetic millimeter very long baseline interferometry observations of the EHT. Our ground-truth models are based on GRMHD simulations of Sgr A* and M87* on horizon scales. We investigated how well Zingularity neural networks are able to infer key model parameters from EHT ob…

Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching
Giacomo Meanti, Thomas Ryckeboer, Michael Arbel, Julien Mairal
https://arxiv.org/abs/2506.14605

Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching
This work addresses image restoration tasks through the lens of inverse problems using unpaired datasets. In contrast to traditional approaches -- which typically assume full knowledge of the forward model or access to paired degraded and ground-truth images -- the proposed method operates under minimal assumptions and relies only on small, unpaired datasets. This makes it particularly well-suited for real-world scenarios, where the forward model is often unknown or misspecified, and collecting…

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features
Miguel A. Lago, Ghada Zamzmi, Brandon Eich, Jana G. Delfino
https://arxiv.org/abs/2506.13917

Evaluating Explainability: A Framework for Systematic Assessment and Reporting of Explainable AI Features
Explainability features are intended to provide insight into the internal mechanisms of an AI device, but there is a lack of evaluation techniques for assessing the quality of provided explanations. We propose a framework to assess and report explainable AI features. Our evaluation framework for AI explainability is based on four criteria: 1) Consistency quantifies the variability of explanations to similar inputs, 2) Plausibility estimates how close the explanation is to the ground truth, 3) F…

Stochastically Dominant Peer Prediction
Yichi Zhang, Shengwei Xu, David Pennock, Grant Schoenebeck
https://arxiv.org/abs/2506.02259 https://

Stochastically Dominant Peer Prediction
Eliciting reliable human feedback is essential for many machine learning tasks, such as learning from noisy labels and aligning AI systems with human preferences. Peer prediction mechanisms incentivize truthful reporting without ground truth verification by scoring agents based on correlations with peers. Traditional mechanisms, which ensure that truth-telling maximizes the expected scores in equilibrium, can elicit honest information while assuming agents' utilities are linear functions of the…

Not all those who drift are lost: Drift correction and calibration scheduling for the IoT
Aaron Hurst, Andrey V. Kalinichev, Klaus Koren, Daniel E. Lucani
https://arxiv.org/abs/2506.09186

Not all those who drift are lost: Drift correction and calibration scheduling for the IoT
Sensors provide a vital source of data that link digital systems with the physical world. However, as sensors age, the relationship between what they measure and what they output changes. This is known as sensor drift and poses a significant challenge that, combined with limited opportunity for re-calibration, can severely limit data quality over time. Previous approaches to drift correction typically require large volumes of ground truth data and do not consider measurement or prediction uncer…

This https://arxiv.org/abs/2410.19214 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

BTS: A Comprehensive Benchmark for Tie Strength Prediction
The rapid rise of online social networks underscores the need to understand the heterogeneous strengths of online relationships. Yet, efforts to assess tie strength (TS) are hindered by the lack of ground-truth labels, differing research perspectives, and limited model performance in real-world settings. To address this gap, we introduce BTS, a comprehensive Benchmark for Tie Strength prediction, aiming to establish a standardized foundation for evaluating and advancing TS prediction methodolog…

A Data-Driven Diffusion-based Approach for Audio Deepfake Explanations
Petr Grinberg, Ankur Kumar, Surya Koppisetti, Gaurav Bharaj
https://arxiv.org/abs/2506.03425

A Data-Driven Diffusion-based Approach for Audio Deepfake Explanations
Evaluating explainability techniques, such as SHAP and LRP, in the context of audio deepfake detection is challenging due to lack of clear ground truth annotations. In the cases when we are able to obtain the ground truth, we find that these methods struggle to provide accurate explanations. In this work, we propose a novel data-driven approach to identify artifact regions in deepfake audio. We consider paired real and vocoded audio, and use the difference in time-frequency representation as th…

Whose Name Comes Up? Auditing LLM-Based Scholar Recommendations
Daniele Barolo, Chiara Valentin, Fariba Karimi, Luis Gal\'arraga, Gonzalo G. M\'endez, Lisette Esp\'in-Noboa
https://arxiv.org/abs/2506.00074

Whose Name Comes Up? Auditing LLM-Based Scholar Recommendations
This paper evaluates the performance of six open-weight LLMs (llama3-8b, llama3.1-8b, gemma2-9b, mixtral-8x7b, llama3-70b, llama3.1-70b) in recommending experts in physics across five tasks: top-k experts by field, influential scientists by discipline, epoch, seniority, and scholar counterparts. The evaluation examines consistency, factuality, and biases related to gender, ethnicity, academic popularity, and scholar similarity. Using ground-truth data from the American Physical Society and Open…

Benchmarking Time-localized Explanations for Audio Classification Models
Cecilia Bola\~nos, Leonardo Pepino, Martin Meza, Luciana Ferrer
https://arxiv.org/abs/2506.04391

Benchmarking Time-localized Explanations for Audio Classification Models
Most modern approaches for audio processing are opaque, in the sense that they do not provide an explanation for their decisions. For this reason, various methods have been proposed to explain the outputs generated by these models. Good explanations can result in interesting insights about the data or the model, as well as increase trust in the system. Unfortunately, evaluating the quality of explanations is far from trivial since, for most tasks, there is no clear ground truth explanation to u…

Do-PFN: In-Context Learning for Causal Effect Estimation
Jake Robertson, Arik Reuter, Siyuan Guo, Noah Hollmann, Frank Hutter, Bernhard Sch\"olkopf
https://arxiv.org/abs/2506.06039

Do-PFN: In-Context Learning for Causal Effect Estimation
Estimation of causal effects is critical to a range of scientific disciplines. Existing methods for this task either require interventional data, knowledge about the ground truth causal graph, or rely on assumptions such as unconfoundedness, restricting their applicability in real-world settings. In the domain of tabular machine learning, Prior-data fitted networks (PFNs) have achieved state-of-the-art predictive performance, having been pre-trained on synthetic data to solve tabular prediction…

STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving
Christian Fruhwirth-Reisinger, Du\v{s}an Mali\'c, Wei Lin, David Schinagl, Samuel Schulter, Horst Possegger
https://arxiv.org/abs/2506.06218

STSBench: A Spatio-temporal Scenario Benchmark for Multi-modal Large Language Models in Autonomous Driving
We introduce STSBench, a scenario-based framework to benchmark the holistic understanding of vision-language models (VLMs) for autonomous driving. The framework automatically mines pre-defined traffic scenarios from any dataset using ground-truth annotations, provides an intuitive user interface for efficient human verification, and generates multiple-choice questions for model evaluation. Applied to the NuScenes dataset, we present STSnu, the first benchmark that evaluates the spatio-temporal …

Instance-Specific Test-Time Training for Speech Editing in the Wild
Taewoo Kim, Uijong Lee, Hayoung Park, Choongsang Cho, Nam In Park, Young Han Lee
https://arxiv.org/abs/2506.13295

Instance-Specific Test-Time Training for Speech Editing in the Wild
Speech editing systems aim to naturally modify speech content while preserving acoustic consistency and speaker identity. However, previous studies often struggle to adapt to unseen and diverse acoustic conditions, resulting in degraded editing performance in real-world scenarios. To address this, we propose an instance-specific test-time training method for speech editing in the wild. Our approach employs direct supervision from ground-truth acoustic features in unedited regions, and indirect …

This https://arxiv.org/abs/2308.03734 has been replaced.
link: https://scholar.google.com/scholar?q=a

Labeling without Seeing? Blind Annotation for Privacy-Preserving Entity Resolution
The entity resolution problem requires finding pairs across datasets that belong to different owners but refer to the same entity in the real world. To train and evaluate solutions (either rule-based or machine-learning-based) to the entity resolution problem, generating a ground truth dataset with entity pairs or clusters is needed. However, such a data annotation process involves humans as domain oracles to review the plaintext data for all candidate record pairs from different parties, which…

Digital twins enable full-reference quality assessment of photoacoustic image reconstructions
Janek Gr\"ohl, Leonid Kunyansky, Jenni Poimala, Thomas R. Else, Francesca Di Cecio, Sarah E. Bohndiek, Ben T. Cox, Andreas Hauptmann
https://arxiv.org/abs/2505.24514

Digital twins enable full-reference quality assessment of photoacoustic image reconstructions
Quantitative comparison of the quality of photoacoustic image reconstruction algorithms remains a major challenge. No-reference image quality measures are often inadequate, but full-reference measures require access to an ideal reference image. While the ground truth is known in simulations, it is unknown in vivo, or in phantom studies, as the reference depends on both the phantom properties and the imaging system. We tackle this problem by using numerical digital twins of tissue-mimicking phan…

Reliable Evaluation of MRI Motion Correction: Dataset and Insights
Kun Wang, Tobit Klug, Stefan Ruschke, Jan S. Kirschke, Reinhard Heckel
https://arxiv.org/abs/2506.05975

Reliable Evaluation of MRI Motion Correction: Dataset and Insights
Correcting motion artifacts in MRI is important, as they can hinder accurate diagnosis. However, evaluating deep learning-based and classical motion correction methods remains fundamentally difficult due to the lack of accessible ground-truth target data. To address this challenge, we study three evaluation approaches: real-world evaluation based on reference scans, simulated motion, and reference-free evaluation, each with its merits and shortcomings. To enable evaluation with real-world motio…

This https://arxiv.org/abs/2410.19214 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

BTS: A Comprehensive Benchmark for Tie Strength Prediction
The rapid rise of online social networks underscores the need to understand the heterogeneous strengths of online relationships. Yet, efforts to assess tie strength (TS) are hindered by the lack of ground-truth labels, differing research perspectives, and limited model performance in real-world settings. To address this gap, we introduce BTS, a comprehensive Benchmark for Tie Strength prediction, aiming to establish a standardized foundation for evaluating and advancing TS prediction methodolog…

This https://arxiv.org/abs/2503.09492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Learning Cascade Ranking as One Network
Cascade Ranking is a prevalent architecture in large-scale top-k selection systems like recommendation and advertising platforms. Traditional training methods focus on single-stage optimization, neglecting interactions between stages. Recent advances have introduced interaction-aware training paradigms, but still struggle to 1) align training objectives with the goal of the entire cascade ranking (i.e., end-to-end recall of ground-truth items) and 2) learn effective collaboration patterns for d…

This https://arxiv.org/abs/2505.24739 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRI
Accurate placental segmentation is essential for quantitative analysis of the placenta. However, this task is particularly challenging in T2*-weighted placental imaging due to: (1) weak and inconsistent boundary contrast across individual echoes; (2) the absence of manual ground truth annotations for all echo times; and (3) motion artifacts across echoes caused by fetal and maternal movement. In this work, we propose a contrast-augmented segmentation framework that leverages complementary infor…

This https://arxiv.org/abs/2503.09492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Learning Cascade Ranking as One Network
Cascade Ranking is a prevalent architecture in large-scale top-k selection systems like recommendation and advertising platforms. Traditional training methods focus on single-stage optimization, neglecting interactions between stages. Recent advances have introduced interaction-aware training paradigms, but still struggle to 1) align training objectives with the goal of the entire cascade ranking (i.e., end-to-end recall of ground-truth items) and 2) learn effective collaboration patterns for d…

Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRI
Xinliu Zhong, Ruiying Liu, Emily S. Nichols, Xuzhe Zhang, Andrew F. Laine, Emma G. Duerden, Yun Wang
https://arxiv.org/abs/2505.24739

Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRI
Accurate placental segmentation is essential for quantitative analysis of the placenta. However, this task is particularly challenging in T2*-weighted placental imaging due to: (1) weak and inconsistent boundary contrast across individual echoes; (2) the absence of manual ground truth annotations for all echo times; and (3) motion artifacts across echoes caused by fetal and maternal movement. In this work, we propose a contrast-augmented segmentation framework that leverages complementary infor…