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A Multi-Institutional Open-Source Benchmark Dataset for Breast Cancer Clinical Decision Support using Synthetic Correlated Diffusion Imaging Data

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arXiv 2023年

作者： Tai, Chi-En Amy Gunraj, Hayden Wong, Alexander Vision and Image Processing Lab University of Waterloo Canada

Recently, a new form of magnetic resonance imaging (MRI) called synthetic correlated diffusion (CDIs) imaging was introduced and showed considerable promise for clinical decision support for cancers such as prostate cancer when compared to current gold-standard MRI techniques. However, the efficacy for CDIs for other forms of cancers such as breast cancer has not been as well-explored nor have CDIs data been previously made publicly available. Motivated to advance efforts in the development of computer-aided clinical decision support for breast cancer using CDIs, we introduce Cancer-Net BCa, a multi-institutional open-source benchmark dataset of volumetric CDIs imaging data of breast cancer patients. Cancer-Net BCa contains CDIs volumetric images from a pre-treatment cohort of 253 patients across ten institutions, along with detailed annotation metadata (the lesion type, genetic subtype, longest diameter on the MRI (MRLD), the Scarff-Bloom-Richardson (SBR) grade, and the post-treatment breast cancer pathologic complete response (pCR) to neoadjuvant chemotherapy). We further examine the demographic and tumour diversity of the Cancer-Net BCa dataset to gain deeper insights into potential biases. Cancer-Net BCa is publicly available as a part of a global open-source initiative dedicated to accelerating advancement in machine learning to aid clinicians in the fight against cancer. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

SAMJAM: Zero-Shot Video Scene Graph Generation for Egocentric Kitchen Videos

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arXiv 2025年

作者： Li, Joshua Cantu, Fernando Jose Pena Yu, Emily Wong, Alexander Cui, Yuchen Chen, Yuhao Vision and Image Processing Lab University of Waterloo Canada Robot Intelligence Lab UCLA United States

Video Scene Graph Generation (VidSGG) is an important topic in understanding dynamic kitchen environments. Current models for VidSGG require extensive training to produce scene graphs. Recently, vision Language Models (VLM) and vision Foundation Models (VFM) have demonstrated impressive zero-shot capabilities in a variety of tasks. However, VLMs like Gemini struggle with the dynamics for VidSGG, failing to maintain stable object identities across frames. To overcome this limitation, we propose SAMJAM, a zero-shot pipeline that combines SAM2’s temporal tracking with Gemini’s semantic understanding. SAM2 also improves upon Gemini’s object grounding by producing more accurate bounding boxes. In our method, we first prompt Gemini to generate a frame-level scene graph. Then, we employ a matching algorithm to map each object in the scene graph with a SAM2-generated or SAM2-propagated mask, producing a temporally-consistent scene graph in dynamic environments. Finally, we repeat this process again in each of the following frames. We empirically demonstrate that SAMJAM outperforms Gemini by 8.33% in mean recall on the EPIC-KITCHENS and EPIC-KITCHENS-100 datasets. © 2025, CC BY.

关键词：

How Much You Ate? Food Portion Estimation on Spoons

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arXiv 2024年

作者： Sharma, Aaryam Czarnecki, Chris Chen, Yuhao Xi, Pengcheng Xu, Linlin Wong, Alexander Vision and Image Processing Lab University of Waterloo Canada National Research Council Canada

Monitoring dietary intake is a crucial aspect of promoting healthy living. In recent years, advances in computer vision technology have facilitated dietary intake monitoring through the use of images and depth cameras. However, the current state-of-the-art image-based food portion estimation algorithms assume that users take images of their meals one or two times, which can be inconvenient and fail to capture food items that are not visible from a top-down perspective, such as ingredients submerged in a stew. To address these limitations, we introduce an innovative solution that utilizes stationary user-facing cameras to track food items on utensils, not requiring any change of camera perspective after installation. The shallow depth of utensils provides a more favorable angle for capturing food items, and tracking them on the utensil’s surface offers a significantly more accurate estimation of dietary intake without the need for post-meal image capture. The system is reliable for estimation of nutritional content of liquid-solid heterogeneous mixtures such as soups and stews. Through a series of experiments, we demonstrate the exceptional potential of our method as a non-invasive, user-friendly, and highly accurate dietary intake monitoring tool. © 2024, CC BY.

关键词： Cameras

In The Wild Ellipse Parameter Estimation for Circular Dining Plates and Bowls

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arXiv 2024年

作者： Pathiranage, Akil Czarnecki, Chris Chen, Yuhao Xi, Pengcheng Xu, Linlin Wong, Alexander Vision and Image Processing Lab University of Waterloo Canada National Research Council Canada

Ellipse estimation is an important topic in food image processing because it can be leveraged to parameterize plates and bowls, which in turn can be used to estimate camera view angles and food portion sizes. Automatically detecting the elliptical rim of plates and bowls and estimating their ellipse parameters for data in-the-wild is challenging: diverse camera angles and plate shapes could have been used for capture, noisy background, multiple non-uniform plates and bowls in the image could be present. Recent advancements in foundational models offer promising capabilities for zero-shot semantic understanding and object segmentation. However, the output mask boundaries for plates and bowls generated by these models often lack consistency and precision compared to traditional ellipse fitting methods. In this paper, we combine ellipse fitting with semantic information extracted by zero-shot foundational models and propose WildEllipseFit, a method to detect and estimate the elliptical rim for plate and bowl. Evaluation on the proposed Yummly-ellipse dataset demonstrates its efficacy and zero-shot capability in real-world scenarios. © 2024, CC BY.

关键词： Parameter estimation

Domain-Guided Masked Autoencoders for Unique Player Identification

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arXiv 2024年

作者： Balaji, Bavesh Bright, Jerrin Rambhatla, Sirisha Chen, Yuhao Wong, Alexander Zelek, John Clausi, David A. Vision and Image Processing Lab University of Waterloo Canada Critical ML Lab University of Waterloo Canada

Unique player identification is a fundamental module in vision-driven sports analytics. Identifying players from broadcast videos can aid with various downstream tasks such as player assessment, in-game analysis, and broadcast production. However, automatic detection of jersey numbers using deep features is challenging primarily due to: a) motion blur, b) low resolution video feed, and c) occlusions. With their recent success in various vision tasks, masked autoencoders (MAEs) have emerged as a superior alternative to conventional feature extractors. However, most MAEs simply zero-out image patches either randomly or focus on where to mask rather than how to mask. Motivated by human vision, we devise a novel domain-guided masking policy for MAEs termed dMAE to facilitate robust feature extraction in the presence of motion blur for player identification. We further introduce a new spatio-temporal network leveraging our novel d-MAE for unique player identification. We conduct experiments on three large-scale sports datasets, including a curated baseball dataset, the SoccerNet dataset, and an in-house ice hockey dataset. We preprocess the datasets using an upgraded keyframe identification (KfID) module by focusing on frames containing jersey numbers. Additionally, we propose a keyframe-fusion technique to augment keyframes, preserving spatial and temporal context. Our spatio-temporal network showcases significant improvements, surpassing the current state-of-the-art by 8.58%, 4.29%, and 1.20% in the test set accuracies, respectively. Rigorous ablations highlight the effectiveness of our domain-guided masking approach and the refined KfID module, resulting in performance enhancements of 1.48% and 1.84% respectively, compared to original architectures. © 2024, CC BY.

关键词： image enhancement

A Moving Camera and Synthetic Calibration Target Solution for Non-Planar Scene Estimation and Projector Calibration 59th

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A Moving Camera and Synthetic Calibration Target Solution fo...

59th International Symposium, Seminar and Exhibition, Display Week 2022

作者： Arnold, Katherine Fieguth, Paul Lamm, Mark Vision and Image Processing Lab University of Waterloo WaterlooON Canada Christie Digital Systems Canada Inc KitchenerON Canada

The automatic calibration of projector systems improves the possible environments available to this technology which relies on exact scene calibration. This paper explores the challenge of projector calibration and non-planar scene estimation. This formulation assumes no prior information on the moving camera or fixed projector. Limited scene understanding provides the basis for constructing synthetic calibration targets to perform a geometric recovery through bundle adjustment. Synthetic data are generated from ground-truth camera and projector parameters to explore the method’s performance over varying properties of the synthetic calibration targets. © 2022 SID.

关键词： Cameras

Particle-Filtering-based Latent Diffusion for Inverse Problems

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arXiv 2024年

作者： Nazemi, Amir Sepanj, Mohammad Hadi Pellegrino, Nicholas Czarnecki, Chris Fieguth, Paul Vision & Image Processing Lab Department of Systems Design Engineering University of Waterloo WaterlooON Canada

Current strategies for solving image-based inverse problems apply latent diffusion models to perform posterior sampling. However, almost all approaches make no explicit attempt to explore the solution space, instead drawing only a single sample from a Gaussian distribution from which to generate their solution. In this paper, we introduce a particle-filtering-based framework for a nonlinear exploration of the solution space in the initial stages of reverse SDE methods. Our proposed particle-filtering-based latent diffusion (PFLD) method and proposed problem formulation and framework can be applied to any diffusion-based solution for linear or nonlinear inverse problems. Our experimental results show that PFLD outperforms the SoTA solver PSLD on the FFHQ-1K and imageNet-1K datasets on inverse problem tasks of super resolution, Gaussian debluring and inpainting. © 2024, CC BY-NC-ND.

关键词： Wiener filtering

NutritionVerse-Thin: An Optimized Strategy for Enabling Improved Rendering of 3D Thin Food Models

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arXiv 2023年

作者： Tai, Chi-En Amy Li, Jason Kumar, Sriram Nair, Saeejith Chen, Yuhao Xi, Pengcheng Wong, Alexander Vision and Image Processing Lab University of Waterloo Canada National Research Council Canada

With the growth in capabilities of generative models, there has been growing interest in using photo-realistic renders of common 3D food items to improve downstream tasks such as food printing, nutrition prediction, or management of food wastage. Despite 3D modelling capabilities being more accessible than ever due to the success of NeRF based view-synthesis, such rendering methods still struggle to correctly capture thin food objects, often generating meshes with significant holes. In this study, we present an optimized strategy for enabling improved rendering of thin 3D food models, and demonstrate qualitative improvements in rendering quality. Our method generates the 3D model mesh via a proposed thin-object-optimized differentiable reconstruction method and tailors the strategy at both the data collection and training stages to better handle thin objects. While simple, we find that this technique can be employed for quick and highly consistent capturing of thin 3D objects. Copyright © 2023, The Authors. All rights reserved.

关键词： 3D modeling

NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimation

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arXiv 2023年

作者： Tai, Chi-En Amy Keller, Matthew Kerrigan, Mattie Chen, Yuhao Nair, Saeejith Xi, Pengcheng Wong, Alexander Vision and Image Processing Lab University of Waterloo Canada National Research Council Canada

77% of adults over 50 want to age in place today, presenting a major challenge to ensuring adequate nutritional intake. It has been reported that one in four older adults that are 65 years or older are malnourished and given the direct link between malnutrition and decreased quality of life, there have been numerous studies conducted on how to efficiently track nutritional intake of food. Recent advancements in machine learning and computer vision show promise of automated nutrition tracking methods of food, but require a large high-quality dataset in order to accurately identify the nutrients from the food on the plate. Unlike existing datasets, a collection of 3D models with nutritional information allow for view synthesis to create an infinite number of 2D images for any given viewpoint/camera angle along with the associated nutritional information. In this paper, we develop a methodology for collecting high-quality 3D models for food items with a particular focus on speed and consistency, and introduce NutritionVerse-3D, a large-scale high-quality high-resolution dataset of 105 3D food models, in conjunction with their associated weight, food name, and nutritional value. These models allow for large quantity food intake scenes, diverse and customizable scene layout, and an infinite number of camera settings and lighting conditions. NutritionVerse-3D is publicly available as a part of an open initiative to accelerate machine learning for nutrition sensing. Copyright © 2023, The Authors. All rights reserved.

关键词： Nutrition