arXiv AI
Aug 11

Compositional Cross-Modality Translation via Whole-Volume Multitask Latent Flow Matching

arXiv:2608. 08135v1 Announce Type: cross Abstract: Cross-modality medical image translation can reduce the burden of multi-modal acquisitions, yet the field remains constrained by two coupled limitations: methods operate on 2D slices or 3D patches rather than whole volumes, and train a separate model for each translation task.

By Daniele Molino, Alessio Zoboli, Camillo Maria Caruso, Valerio Guarrasi, Paolo Soda
arXiv AI
4d ago

Pixel Wised Lesion Prediction on COVID-19 CT Imagery: A Comparative Analysis of Automated Image Segmentation Architectures

The study evaluates four deep‑learning segmentation architectures—Unet, PSPNet, Linknet, and FPN—paired with six pre‑trained encoders to predict COVID‑19 lesions in CT images. Experiments on three COVID‑19 CT datasets show high accuracy, achieving a maximum binary F1‑score of 98% and multi‑class F1‑scores of 75% and 77%. The work aims to provide a standardized performance benchmark for medical image segmentation and a reference for other imaging scenarios.

By Sarmad Khan, Basim Azam, Arslan Shaukat
Hugging Face Trending Papers
Jul 1

MedCAGD: Context-Aware Gated Decoder for Efficient Medical Image Segmentation

Medical image segmentation relies on the ability of encoder-decoder architectures to translate rich feature representations into accurate pixel-level predictions under challenging conditions such as low contrast, structural ambiguity, and scale variability. While recent advances in large-scale pretraining and transformer-based encoders have substantially improved feature extraction, segmentation accuracy remains constrained by decoder design, particularly in terms of cross-scale alignment, contextual integration, and boundary preservation.