LePaX enables high-resolution chest X-ray report generation by learning to allocate resolution to diagnostically relevant regions and fusing high-res patches back into global features without increasing token count.
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44 Pith papers cite this work. Polarity classification is still indexing.
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representative citing papers
IS-SNN removes activation normalization from deep SNNs via topology-aware weight standardization folded into static weights, matching dynamic BN accuracy on ImageNet (68.05%) while cutting FPGA neuron LUT usage by 96.4%.
A^4D detects adversarial attacks in an attack- and classifier-agnostic way by measuring non-arbitrary shifts in CLIP embedding space from prompt-based similarity scores.
Introduces synthetic ground-truth dataset for CAM evaluation, proposes ARCC composite metric, and RefineCAM method that aggregates layers for higher-resolution maps outperforming baselines.
PromptDx adds a differentiable adapter to align multimodal data with a pre-trained TabPFN-style ICL engine, achieving strong Alzheimer's diagnosis performance with only 1% context samples.
MoCA3D formulates monocular 3D box prediction as dense pixel-space tasks using corner heatmaps and depth maps, with a new PAG metric for image-plane evaluation.
Hybrid T2I generation with teacher-student pseudo-labeling plus VRAIN context-aware I2I rare-class editing improves LVIS instance segmentation AP, especially on rare categories.
Echo4DIR reconstructs continuous 4D cardiac geometry from sparse 2D echocardiography videos using implicit representations, epipolar feature fusion, self-supervised domain adaptation, and radial SDF alignment to achieve up to 98.35% Dice overlap.
Mirage auditing framework reveals that VFL unlearning methods passing output-level certification retain substantial class structure in representations, with no method achieving high utility plus both output and representation forgetting, plus class-sample asymmetry in residual traces.
Pretrained autoencoders in medical latent diffusion encode discriminative features well for reconstruction but structure their latent spaces in ways that hinder classifier learning, a gap that persists across architectures and is not closed by domain fine-tuning.
A verifiable CBM framework grounds concepts in localized image patches, achieving comparable accuracy to standard CBMs on medical benchmarks while enabling direct inspection of concept correctness.
HashSCD is a patch-wise hashing method for unsupervised scene change detection and localization that operates directly in Hamming space with competitive performance and lower computational cost.
Counterfactual stress testing with causal generative models offers a more accurate proxy than simple perturbations for predicting medical image model performance under distribution shifts.
Hyp2Former learns hierarchical semantic similarities in hyperbolic space among known categories so that unknown objects remain close to higher-level concepts and can be detected reliably.
An asymmetric multi-level distillation framework lets a student ViT approximate clean-image representations from distorted inputs alone, outperforming prior methods on classification under distortions.
Logic Gate Networks produce compact Boolean-circuit descriptors for video copy detection that match or exceed prior accuracy at over 11k inferences per second and orders-of-magnitude smaller size.
LLMasTool improves neural architecture search by evolving code-mined hierarchical trees with diversity-guided Bayesian planning and targeted LLM assistance, reporting gains of 0.69, 1.83, and 2.68 points on CIFAR-10, CIFAR-100, and ImageNet16-120.
ESCAPE combines spatio-temporal fusion mapping for depth-free 3D memory with a memory-driven grounding module and adaptive execution policy to reach 65.09% success on ALFRED test-seen long-horizon mobile manipulation tasks.
A new high-resolution landscape concept dataset enables the first Robust TCAV explanations of deep SDMs, largely validating ecological expectations for Plecoptera and Trichoptera while flagging novel associations.
GroundingAnomaly uses a Spatial Conditioning Module and Gated Self-Attention in a frozen diffusion U-Net to synthesize spatially accurate few-shot anomalies, reaching SOTA on MVTec AD and VisA for detection, segmentation, and instance detection.
PRISM-CTG is the first large-scale foundation model for cardiotocography that uses multi-view self-supervised learning on unlabeled data to learn transferable representations, outperforming baselines on seven downstream tasks with external validation.
Hierarchical clinical-reasoning SFT plus counterfactual GRPO yields SoTA diagnostic accuracy for multimodal LLMs on gastrointestinal endoscopy benchmarks.
ShapeFuse uses bidirectional cross-modal temporal attention and adaptive gating to fuse deformable shape and texture features for cardiac video classification, outperforming existing fusion strategies on a cine CMR dataset.
FrameONE introduces hierarchical intra-view and inter-view motion modeling for end-to-end multi-view echocardiographic keyframe detection, reporting SOTA accuracy on 25,872 videos from four views with code released.
citing papers explorer
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From Image Hashing to Scene Change Detection
HashSCD is a patch-wise hashing method for unsupervised scene change detection and localization that operates directly in Hamming space with competitive performance and lower computational cost.
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Counterfactual Stress Testing for Image Classification Models
Counterfactual stress testing with causal generative models offers a more accurate proxy than simple perturbations for predicting medical image model performance under distribution shifts.
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FedSurrogate: Backdoor Defense in Federated Learning via Layer Criticality and Surrogate Replacement
FedSurrogate defends federated learning against backdoors by clustering on security-critical layers and substituting malicious updates with benign surrogates, reporting false-positive rates below 10% and attack success below 2.1% under non-IID conditions.