Medicine & Health
463 items
VesselSim trains a 3D segmentation model on synthetic vessels only, matching vascular foundation models zero-shot on real brain and kidney MR/CT
VesselSim proposes a two-stage framework for universal 3D blood vessel segmentation: a stochastic, geometry-driven vascular simulation (recursive branching, curvature-controlled growth, collision-aware topology) with domain-randomized intensity synthesis generates 16,500 anatomically plausible 3D angiographic volumes, a 3D U-Net is trained solely on this synthetic data, and a test-time adaptation strategy via a self-supervised mask reconstruction decoder adapts at inference, achieving zero-shot performance competitive with state-of-the-art vascular segmentation foundation models on multiple real datasets spanning MR and CT and several anatomical regions including the brain and kidneys, without real annotated data during training.
HANAMI predicts drug-gene-disease motifs with heterogeneous graph contrastive learning, improving up to about 6% over state-of-the-art baselines and holding an ~18% edge in zero-shot settings
The authors present HANAMI (Heterogeneous grAph coNtrastive leArning for drug-gene-disease Motif predIction), a multi-view deep graph learning framework that integrates heterogeneous biomedical knowledge such as chemical structures, genomic sequences, and clinical phenotypes and uses relation-aware topology encoding, structure-aware aggregation, and contrastive learning to predict drug-gene-disease motifs; systematic evaluation on benchmark datasets shows up to about 6% improvement over existing state-of-the-art methods in motif prediction, an approximately 18% performance advantage maintained in zero-shot settings involving previously unseen entities, and the ability to prioritize drug-disease relationships investigated in Phase II or III trials while identifying candidate genes suggestin
SCISSR swaps point and box prompts for scribbles, reaching 95.41% Dice on EndoVis 2018 and 96.30% Dice on cross-domain CholecSeg8k
The work presents SCISSR, a scribble-promptable framework for interactive surgical scene segmentation: a lightweight Scribble Encoder turns freehand scribbles into dense prompt embeddings compatible with the mask decoder, and together with Spatial Gated Fusion and toggleable LoRA adapters it supports multi-round correction over a frozen SAM 2 backbone, reaching 95.41% Dice on EndoVis 2018 with five interaction rounds and 96.30% Dice on the unseen CholecSeg8k with three rounds, outperforming iterative point prompting on both benchmarks.
L2L-Flow moves volumetric stochastic segmentation into latent space: about 14x faster radiotherapy-target inference with a competitive GED of 0.161
The work introduces Latent-to-Latent Flow (L2L-Flow), which first compresses labels into a latent space with a volumetric label autoencoder and then learns a rectified flow between an image-conditional latent prior and frozen latent label representations, yielding stochastic segmentation on a private radiotherapy clinical-target-volume dataset (55 cases, 5-fold cross-validation) and the CURVAS multi-organ dataset (90 cases), with inference about 14x faster than full-resolution Flow-SSN (0.936 s/image versus 15.296 s/image) at a GED of 0.161, alongside a time-shifted noise schedule that improves Flow-SSN stability on high-dimensional volumetric data.
DA-SAM3 routes over dual-adaptive low-rank experts to gain about 5% accuracy while cutting MoE parameter overhead by over 80% on four public medical segmentation benchmarks
The work proposes Dual-Adaptive SAM3 (DA-SAM3), which replaces selected feed-forward blocks in SAM3's fusion module with dual-adaptive MoE layers: a task-aware Dynamic Expert Router (DER) sparsely activates experts by jointly reasoning over visual content and the textual concept prompt, while a parameter-aware Decomposed Parameterized Experts (DPE) design represents each expert as a shared frozen base inherited from pretrained SAM3 plus a lightweight trainable low-rank delta, so that on the four public datasets Synapse CT, MMWHS, BTCV and ACDC it matches or exceeds fully fine-tuned SAM3 and standard MoE baselines, reports roughly a 5% gain over then state-of-the-art methods, and reduces MoE parameter overhead by more than 80%.
PC-Seg lifts sparse 2D annotations to 3D OCT segmentation via five-stage curriculum learning, matching full supervision with about 0.7% of labels
The work proposes PC-Seg (Progressive Cross-view Segmentation), a five-stage curriculum learning framework in which a single 2D model first learns cross-view consistency between standard B-scans and orthogonal slices to generate reliable volumetric pseudo-labels, which are then distilled into a 3D model and followed by 2D/3D co-training with ensemble pseudo-labeling; on the public MSHC and Duke DME OCT datasets it reaches segmentation accuracy comparable to fully supervised learning using only about 0.7% of the labeled data, outperforming the semi-supervised and retinal layer segmentation methods it compares against.
MDM2 promoter P1/P2 switching and colorectal cancer lineage plasticity: deep-learning morphology classification at 98.5% accuracy, with higher P2 index in TP53 wild-type tumors and greater Nutlin-3a sensitivity
Using 63 organoid samples from 22 colorectal cancer patients, external validation in TCGA-COAD/READ (n=624) and GSE39582 (n=536) totaling 1,160 cases, public cell line panels (GDSC2, DepMap), and 65 lines from an independent patient-derived CRC organoid biobank, the study tested whether usage of the dual MDM2 promoters (P1/P2) acts as a molecular switch separating a chromosomal-instability type from an environment-adaptive type (microsatellite instability/serrated pathway with gastric metaplasia), finding deep-learning morphological classification at 98.5% test accuracy (64/65), morphology corresponding to P1/P2 isoform usage (median Type1 fraction 0.826 versus 0.444 in P1-dominant samples; non-Type1 cystic mucinous morphology in P2-dominant samples, AUC 0.
CalcSeg combines confidence-aware curriculum learning with slice-wise self-attention to raise myocardial scar segmentation Dice to 0.677 and low-confidence Dice to 0.644 on single-stack LGE-CMR
The work presents CalcSeg, a confidence-aware latent 3D context curriculum learning framework that scores each sample using Dice, percentage scar-burden error, and epistemic uncertainty from Monte Carlo Dropout, expands training from easy to hard cases across stages, and uses slice-wise self-attention to infer subject-level 3D anatomical context from single-stack 2D LGE-CMR; on LGE-CMR data from four sites and two segmentation challenges (MICCAI 2012 LV Infarct, EMIDEC 2020) comprising 976 patients, it reaches myocardial scar Dice of 0.677±0.24, low-confidence Dice of 0.644±0.22, scar error of 38.88%, and low-confidence scar error of 35.03%, outperforming TransUNet, AttentionUNet, UNETR, ScarNet, and ScarNet with supervised curriculum learning using expert difficulty labels.
KANResDiff combines KAN spline time encoding with a local Schrödinger bridge for residual diffusion, cutting GED by up to 18.8% and raising HM-IoU32 by up to 7.7% on LIDC and ISIC3
The work proposes KANResDiff, which learns local residual diffusion via Kolmogorov-Arnold Networks for ambiguous medical image segmentation: it replaces MLP linear time embeddings with B-spline-based Independent Time Encoding to strengthen independence across inference stages, and injects a deterministic residual prior with learnable weights through a Residual Schrödinger Bridge, achieving state-of-the-art GED and HM-IoU on the two public datasets LIDC and ISIC3 with maximum improvements of 16.8% and 7.7% respectively while keeping competitive MDM performance.
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