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- Video Enhancement with Task-Oriented Flow1,802
T task-oriented flow (TOFlow), a motion representation learned in a self-supervised, task-specific manner, is proposed, which outperforms traditional optical flow on standard benchmarks as well as the Vimeo-90K dataset in three video processing tasks.
- MedMNIST v2 - A large-scale lightweight benchmark for 2D and 3D biomedical image classification1,572
Covering primary data modalities in biomedical images, MedMNIST v2 is designed to perform classification on lightweight 2D and 3D images with various dataset scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression, and multi-label).
- Learning and Using the Arrow of Time369
A ConvNet suitable for extended temporal footprints and for class activation visualization, and the effect of artificial cues, such as cinematographic conventions, on learning is studied, which achieves state-of-the-art performance on large-scale real-world video datasets.
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- A connectomic study of a petascale fragment of human cerebral cortex241
In studying these neurons, it is found that deep layer excitatory cell types can be classified into new subsets, based on structural and connectivity differences, and that chandelier interneurons not only innervate excitatories neuron initial segments as previously described, but also each other’s initial segments.
- Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution138
A dynamic high-pass filtering module that locally applies adaptive filter weights for each spatial location and channel group to preserve high-frequency signals and a matrix multi-spectral channel attention module that predicts the attention map of features decomposed in the frequency domain.
- MitoEM Dataset: Large-Scale 3D Mitochondria Instance Segmentation from EM Images132
The MitoEM dataset is introduced, a 3D mitochondria instance segmentation dataset with two (30μm)3 volumes from human and rat cortices respectively, 3, 600× larger than previous benchmarks.
- Seeing the Arrow of Time116
Good video forwards/backwards classification results are demonstrated on a selection of YouTube video clips, and on natively-captured sequences (with no temporally-dependent video compression), and what motions the models have learned that help discriminate forwards from backwards time are examined.
- Motion microscopy for visualizing and quantifying small motions94
A computational tool is presented that quantifies tiny motions in videos and then visualizes them by producing a new video in which the motions are made large enough to see, leading to the discovery of previously unknown phenomena.
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- Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training81
Qualitative validation shows that noise generated by PNGAN is highly similar to real noise in terms of intensity and distribution, and an efficient architecture Simple Multi-scale Network (SMNet) as the generator is presented.
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- QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity55
A new style transfer framework for high visual-fidelity stylization called QunntArt, which fusing the quantized and continuous latent representations of the generated artwork toward the centroids of the real artwork distribution with vector quantization.
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- UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues54
Two approaches to improving tissue segmentation that are applicable to multiple deep learning frameworks are demonstrated and cumulatively and substantially improve segmentation on a wide range of tissue types and provide a set of improved segmentation models.
- $R^2$-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding52
Reversed Recurrent Tuning ($R^2$-Tuning), a parameter- and memory-efficient transfer learning framework for video temporal grounding, achieves state-of-the-art performance across three VTG tasks (i.e., moment retrieval, highlight detection, and video summarization) on six public benchmarks.
- Learning Causality-inspired Representation Consistency for Video Anomaly Detection52
A causality-inspired representation consistency (CRC) framework to implicitly learn the unobservable causal variables of normality directly from available normal videos and detect abnormal events with the learned representation consistency.
- VizAbility: Enhancing Chart Accessibility with LLM-based Conversational Interaction50
VizAbility is a novel system that enriches chart content navigation with conversational interaction, enabling users to use natural language for querying visual data trends, and adapts to the user’s navigation context for improved response accuracy and facilitates verbal command-based chart navigation.
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- Dense 4D nanoscale reconstruction of living brain tissue48
An integrated optical/machine-learning technology, LIONESS (live information-optimized nanoscopy enabling saturated segmentation) leverages optical modifications to stimulated emission depletion microscopy in comprehensively, extracellularly labeled tissue and previous information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise ratio and compatibility with living tissue.
- The impact of ChatGPT and LLMs on medical imaging stakeholders: Perspectives and use cases45
This paper introduces an analytic framework for presenting the complex interactions between LLMs and the broader ecosystem of medical imaging stakeholders, including businesses, insurance entities, governments, research institutions, and hospitals (nicknamed BIGR-H).
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- Automated Measurements of Key Morphological Features of Human Embryos for IVF45
This work automates feature extraction of time-lapse microscopy of human embryos with a machine-learning pipeline of five convolutional neural networks (CNNs) that greatly speeds up the measurement of quantitative, biologically relevant features that may aid in embryo selection.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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