HPR-SAM replaces manual prompts in SAM with hierarchical probabilistic anatomical representations, achieving state-of-the-art medical image segmentation on Synapse, LA, and PROMISE12 datasets.
Segment anything in medical images
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
GenMed uses diffusion models to capture P(X,Y) for medical tasks and performs inference via gradient-based test-time optimization, supporting arbitrary observation combinations without retraining.
TinyUSFM distills a large ultrasound foundation model into a lightweight version using feature-gradient coreset selection and domain-separated masked image modeling, matching performance on a new 18-dataset benchmark with 6.36% of the parameters.
DRD introduces a reprogramming module and CKA-based distillation to enable efficient, robust adaptation of medical foundation models to downstream 2D/3D classification and segmentation tasks, outperforming prior PEFT and KD methods on 18 tasks.
TopoMamba improves medical image segmentation by combining topology-aware diagonal scans with standard cross-scans and a HSIC Gate for efficient fusion, yielding gains on thin and curved targets like the pancreas.
citing papers explorer
-
HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation
HPR-SAM replaces manual prompts in SAM with hierarchical probabilistic anatomical representations, achieving state-of-the-art medical image segmentation on Synapse, LA, and PROMISE12 datasets.
-
GenMed: A Pairwise Generative Reformulation of Medical Diagnostic Tasks
GenMed uses diffusion models to capture P(X,Y) for medical tasks and performs inference via gradient-based test-time optimization, supporting arbitrary observation combinations without retraining.
-
TinyUSFM: Towards Compact and Efficient Ultrasound Foundation Models
TinyUSFM distills a large ultrasound foundation model into a lightweight version using feature-gradient coreset selection and domain-separated masked image modeling, matching performance on a new 18-dataset benchmark with 6.36% of the parameters.
-
Deep Reprogramming Distillation for Medical Foundation Models
DRD introduces a reprogramming module and CKA-based distillation to enable efficient, robust adaptation of medical foundation models to downstream 2D/3D classification and segmentation tasks, outperforming prior PEFT and KD methods on 18 tasks.
-
TopoMamba: Topology-Aware Scanning and Fusion for Segmenting Heterogeneous Medical Visual Media
TopoMamba improves medical image segmentation by combining topology-aware diagonal scans with standard cross-scans and a HSIC Gate for efficient fusion, yielding gains on thin and curved targets like the pancreas.