This study addresses critical gaps in automated lymphoma segmentation from PET/CT images, focusing on issues often overlooked in existing literature. While deep learning has been applied for lymphoma lesion segmentation, few studies incorporate out-of-distribution testing, raising concerns about model generalizability...
Shadab Ahamed, Yixi Xu, Sara Kurkowska et al.· 0 citations
OceanDepths is introduced, the first open, global, regridded AI-ready dataset that pairs satellite-derived sea surface temperature, sea surface salinity, and sea surface height products with co-located EN4 subsurface temperature and salinity profiles, complemented by matched GLORYS12 ocean reanalysis data to support co...
Simon Donike, Ruben Cartuyvels, A. I. Ferola et al.· 0 citations
Can neural networks learn algebraic rules from visual inputs, or do they merely fit observed patterns? We study this question using MNIST (or EMNIST letters) as states and modular arithmetic operations as actions in a JEPA-style world model. Standard supervised and JEPA baselines with operation embeddings achieve high...
Divyansh Jha, Yuanfang Xie, Brennen Yu et al.· 0 citations
This work proposes a multi-dimensional, primitive based framework for dynamic contrast-enhanced MRI reconstruction that disentangles the underlying anatomy, the dynamic contrast enhancement, and residual motion into separate temporal basis functions, thereby enabling a geometrical interpretation of the representation.
Veronika Spieker, Wenqi Huang, Cemre Ariyurek et al.· 0 citations
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This work investigates the use of auxiliary k-space magnitude information for accelerated steady-state dynamic MRI reconstruction, and demonstrates strong consistency of k-space magnitudes across time-frames.
Mahdi Saberi, Y. Alçalar, Merve Gülle et al.· 1 citation
An advanced system capable of automatically detecting complicated appendicitis from ultrasound images was developed and was explained with gradient-weighted class activation mapping (Grad-CAM), which creates a heatmap of the regions responsible for the model's prediction of the infected areas.
Fahad Ahammed, Omar Faruq Shikdar, Navid Zaman et al.· 0 citations
A novel mechanism to automatically identify which of these point-labels are suitable, and which are actively harmful, when used for propagation is introduced, paving the way for scalable ecological analysis.
César Borja, Breck A. McCollum, J. Byrnes et al.· 0 citations
Results indicate the visual component is dominant but not solely responsible for skill prediction; further work is needed to conclusively disentangle transferable skill features from those bound to a specific visual domain.
Hanna Hoffmann, F. Bechtolsheim, Stefanie Speidel et al.· 0 citations
Vision-language-action (VLA) and world-action models typically absorb unfamiliar manipulation tasks through additional robot data collection and policy optimization. This recurring retraining burden slows the acquisition of new behavior. We present Teach-and-Grow Learning (TGL), a training-free architecture that turns...
Chang Nie, Zhe Liu, He-Sheng Wang· The international journal of...· 2 citations
Cell mimicry arises when different cell types appear morphologically similar. Human pathologists resolve this ambiguity using surrounding tissue context, whereas current vision models either lack contextual reasoning (cell foundation models) or cannot operate at the cell level (pathology MLLMs). We present Loki-OT, whi...
Xiang Li, Yuqi Wang, Casey C. Heirman et al.· 0 citations
MagViT, an interpretable multi-magnification transformer framework with scale-gated fusion and patient-level model selection, is presented, an interpretable multi-magnification transformer framework with scale-gated fusion and patient-level model selection relative to prior ViT-centered BreakHis work.
Nabil Ashab, Soumitra Kundu, Saif Mahmud Parvez et al.· 0 citations
Optizing Your Sampling (OYS), which instead treats timestep selection as a black-box optimization problem, optimizing the target metric directly with Bayesian optimization, improves both simple and sophisticated samplers such as Euler and DPM-Solver++.
Travis Zhang, Christian K. Belardi, Justin Lovelace et al.· 0 citations
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
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