The paper presents the first systematic evaluation of out‑of‑distribution generalization for congenital heart disease (CHD) segmentation, using the ImageCHD cohort as a held‑out target. It compares several segmentation architectures under different training regimes, showing that in‑distribution performance is a poor predictor of cross‑cohort robustness: nnU‑Net drops from 0.77 to 0.51 Dice, while SwinUNETR maintains higher performance at 0.67 Dice. Limited target‑domain adaptation with only 11 labeled ImageCHD cases boosts all SwinUNETR variants above 0.76 Dice, highlighting the importance of explicit cross‑dataset testing.
By Aniketh Vijesh, Shrisharanyan Vasu, Abhijit Ramesh, Clare Pomeroy-Ward, Harikrishnan Anil Maya, Sarin Xavier, Mahesh Kappanayil, Gilad Gressel
Congenital heart disease (CHD) diagnosis and surgical planning often require patient-specific 3D anatomical models, but manual segmentation is labor-intensive, particularly in complex anatomies. Altho...
The study explores how adding anatomical priors and active learning can improve the accuracy of deep learning models for segmenting the Clinical Target Volume (CTV) in gastric cancer radiotherapy. Using 100 retrospective CT scans, an nnU‑Net model trained on 10 expert‑contoured cases was enhanced with voxel‑wise anatomical prior maps and iterative active learning over four rounds. The combined approach raised the mean Dice Similarity Coefficient from 0.84 to 0.87, demonstrating that both techniques individually and together improve segmentation performance and generalizability.
By Phillip Chlap, Mark Lee, Trevor Leong, Matthew Field, Jason Dowling, Hang Min, Julie Chu, Jennifer Tan, Phillip K. Tran, Tomas Kron, Annette Haworth, Martin A. Ebert, Shalini K. Vinod, Lois Holloway
The paper introduces Segment Anything Small (SAS), a data‑augmentation method that improves deep‑learning segmentation of small anatomical structures in ultrasound images. SAS uses two transformations: resizing and embedding organ thumbnails into a black background to vary organ scale, and adding noise to regions of interest to mimic tissue texture variability. Experiments on one internal and five external datasets show Dice score gains up to 0.35, with an average improvement of 0.16, and demonstrate that SAS enhances model robustness and generalizability without adding hallucinations or artifacts.
By Danielle L. Ferreira, Ahana Gangopadhyay, Hsi-Ming Chang, Ravi Soni, Gopal Avinash
arXiv:2608. 14766v1 Announce Type: cross Abstract: Uncertainty estimation is critical for the safe clinical deployment of deep learning in medical image segmentation, with aleatoric uncertainty theoretically designed to capture irreducible data ambiguity.
By Simon Baur, Arne Schernich, Ekin B\"oke, Wojciech Samek, Jackie Ma
arXiv:2509. 05238v2 Announce Type: replace-cross Abstract: Deep learning (DL) has transformed neuroimaging by delivering state-of-the-art performance with reduced computation times.
By In\'es Gonzalez-Pepe, Vinuyan Sivakolunthu, Yohan Chatelain, Tristan Glatard
arXiv:2608. 10903v1 Announce Type: cross Abstract: Reliable clinical deployment of machine learning requires models that know when they are likely to fail, particularly for subgroups underrepresented in training data.
By Paul Fischer, Ece Ozkan
arXiv:2608.22532v1 Announce Type: new
Abstract: Domain shift across imaging modalities and acquisition sites remains a significant barrier to the clinical deployment of segmentation models. Source-fr...
By Tal Grossman, Noa Cahan, Hayit Greenspan
arXiv:2604. 19191v2 Announce Type: replace-cross Abstract: Deploying AI-based anomaly detection across diverse clinical imaging settings remains challenging because most existing methods rely on modality-specific architectures, anatomical priors, or extensive retraining, limiting their use as general-purpose screening tools.
By Pritam Kar, Gouri Lakshmi S, Saptarshi Bej
arXiv:2609.06807v1 Announce Type: cross
Abstract: In this work, we comprehensively evaluate three popular feature-extraction paradigms in AI-based neuroimaging modeling: (1) computation of anatomical...
By Boyang Yu, Miquel Lopez Escoriza, Long Chen, Arjun V. Masurkar, Narges Razavian, Carlos Fernandez-Granda
arXiv:2609.25850v1 Announce Type: new
Abstract: Deep learning performance generally improves with increasing training data, yet this scaling is fundamentally constrained by annotation cost in large-s...
By Xiaofei Du, Lei Zhang, Shuyu Yan, Manning Wang, Zhijian Song
The paper introduces an unsupervised approach to medical image segmentation by training a Denoising Diffusion Probabilistic Model (DDPM) on 21 unlabeled abdominal CT scans to learn anatomical features. The encoder weights from the DDPM are transferred to a U‑Net for downstream segmentation on the BTCV multi‑organ dataset, resulting in a significant Dice score improvement for liver segmentation from 0.75 to 0.93. In low‑data regimes, diffusion‑pretrained models retain robust performance, achieving high Dice scores even with only 10% of labeled data.
By Akshat G, Divyansh Gupta, Shaleen Bhatnagar, Shilpa Ankalaki, Tusar Kanti Mishra