ScaleMAP is a dimensionality-reduction method that preserves both neighborhood structure and local density by scaling embedding displacements with original local radii, matching DensMAP on density while retaining UMAP-level neighborhood fidelity.
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
10 Pith papers cite this work. Polarity classification is still indexing.
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VitaminP uses paired H&E-mIF data to train a model that transfers molecular boundary information, enabling accurate whole-cell segmentation directly from routine H&E histology across 34 cancer types.
GSHAC performs exact HAC on large geographic point sets by building a sparse geodesic graph and proving that connected-component subproblems yield identical results to the dense algorithm for all standard linkages at cut heights below the sparsity threshold.
A matrix representation of the truncated Lamb operator on a free-edge Bessel basis produces the frequency-domain response of finite circular plates on elastic half-spaces.
ConnectomeBench2 supplies a unified multi-species benchmark of expert proofreading labels and shows a single Vision Transformer achieving human-level performance on split and merge error tasks while providing calibration and distribution-shift diagnostics.
A convergent dictionary learning method with TV and non-negativity constraints achieves 94-97% reconstruction fidelity on multi-channel microscopy data and enables unsupervised lymphoid-myeloid cell separation.
μMatch applies student-teacher semi-supervised methods with foundation models to improve segmentation of mitochondria, nuclei, and neurites in EM images over strong baselines.
MicroDiffuse3D is a foundation model that restores 3D microscopy images under sparse super-resolution, joint degradation, and low-SNR denoising, reporting 10.58% segmentation and 15.59% line-profile gains over baselines.
SwinUNETR outperforms 3D UNet with Dice scores up to 0.902 on larger gland subsets using mixed-cohort five-fold training, while UNETR performs poorly on the same subsets.
Details on-orbit photon background removal algorithms for the Carruthers GCI and reports ~3% error in exospheric radiance from synthetic image validation.
citing papers explorer
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ScaleMAP: Preserving Local Density and Neighborhood Structure in Low-Dimensional Embeddings
ScaleMAP is a dimensionality-reduction method that preserves both neighborhood structure and local density by scaling embedding displacements with original local radii, matching DensMAP on density while retaining UMAP-level neighborhood fidelity.
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VitaminP: cross-modal learning enables whole-cell segmentation from routine histology
VitaminP uses paired H&E-mIF data to train a model that transfers molecular boundary information, enabling accurate whole-cell segmentation directly from routine H&E histology across 34 cancer types.
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Scalable Exact Hierarchical Agglomerative Clustering via Sparse Geographic Distance Graphs
GSHAC performs exact HAC on large geographic point sets by building a sparse geodesic graph and proving that connected-component subproblems yield identical results to the dense algorithm for all standard linkages at cut heights below the sparsity threshold.
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Dynamic Response of a Finite Circular Plate on an Elastic Half-Space Using the Truncated Lamb Kernel
A matrix representation of the truncated Lamb operator on a free-edge Bessel basis produces the frequency-domain response of finite circular plates on elastic half-spaces.
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ConnectomeBench2: A Unified Benchmark for Automated Connectomic Proofreading
ConnectomeBench2 supplies a unified multi-species benchmark of expert proofreading labels and shows a single Vision Transformer achieving human-level performance on split and merge error tasks while providing calibration and distribution-shift diagnostics.
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Learned Dictionaries with Total Variation and Non-Negativity for Single-Cell Microscopy: Convergence Theory and Deterministic Multi-Channel Cell Feature Unification
A convergent dictionary learning method with TV and non-negativity constraints achieves 94-97% reconstruction fidelity on multi-channel microscopy data and enables unsupervised lymphoid-myeloid cell separation.
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$\mu$Match: Foundation Models for Semi-supervised Learning and Domain Adaptation in EM
μMatch applies student-teacher semi-supervised methods with foundation models to improve segmentation of mitochondria, nuclei, and neurites in EM images over strong baselines.
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MicroDiffuse3D: A Foundation Model for 3D Microscopy Imaging Restoration
MicroDiffuse3D is a foundation model that restores 3D microscopy images under sparse super-resolution, joint degradation, and low-SNR denoising, reporting 10.58% segmentation and 15.59% line-profile gains over baselines.
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Improving Prostate Gland Segmentation Using Transformer based Architectures
SwinUNETR outperforms 3D UNet with Dice scores up to 0.902 on larger gland subsets using mixed-cohort five-fold training, while UNETR performs poorly on the same subsets.
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On-orbit Calibration of the Carruthers GCI: Photon Background Removal
Details on-orbit photon background removal algorithms for the Carruthers GCI and reports ~3% error in exospheric radiance from synthetic image validation.