arXiv Computer Vision
LightMIS: Ultra-Lightweight Medical Image Segmentation Without a Stage-Wise Decoder
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Jul 28
DAMamba-UNet3D: A Parameter-Efficient Mamba State Space U-Net with Dynamic Adaptive Scan for 3D Medical Image Segmentation
arXiv:2607. 22718v1 Announce Type: cross Abstract: We propose parameter-efficient SSM-based U-Net architectures for 3D medical image segmentation.
arXiv Computer Vision
Sep 17
LiteViLNet: Lightweight Vision-LiDAR Fusion Network for Efficient Road Segmentation
LiteViLNet is a lightweight RGB‑geometry fusion network for road segmentation that uses a MobileNetV3 RGB encoder and a tiny depth‑wise‑separable geometry encoder. Its multi‑scale fusion module enhances modality‑specific features, performs cross‑modal interaction, and applies adaptive gating, while a depth‑wise large‑kernel bridge expands contextual support with minimal overhead. The U‑Net‑style decoder is trained with deep supervision, achieving state‑of‑the‑art performance on KITTI and ORFD benchmarks and running at up to 68.73 FPS on a Jetson Orin NX with TensorRT FP16.
Hugging Face Trending Papers
Jul 27
LCMamNet: A Lightweight Cross-scale Mamba Network for Infrared Small Target Detection
Infrared small target detection (IRSTD) is important for low-altitude perception, unmanned-system warning, and security monitoring. However, weak targets in infrared imagery usually occupy only a few pixels and are easily submerged by cloud clutter, ground edges, and bright noise, making it difficult for lightweight segmentation-based methods to preserve local target structures while suppressing background interference.
arXiv Computer Vision
Sep 22
VGG16-MCA UNet: Whole-Tumor Segmentation in 2D FLAIR MRI with Decoder-Side Channel Attention
VGG16-MCA UNet is a hybrid neural network that combines an ImageNet‑pretrained VGG16 encoder with a decoder enhanced by a Multi‑Channel Attention module, trained using Focal Tversky loss to address class imbalance. The model was evaluated as a 2‑D, FLAIR‑only whole‑tumor segmenter on BraTS 2020 and LGG datasets, achieving a pixel‑level Dice of 95.10 % on BraTS and 88.32 % on LGG in a 5‑fold cross‑validation setting. Inference time is 66.32 ms per 256×256 slice on a single RTX 2060, only slightly slower than a VGG16‑UNet without attention. whyItMatters":"The study provides a reproducible 2‑D FLAIR baseline for whole‑tumor segmentation, demonstrating high Dice scores and detailed reporting of training and evaluation protocols."
arXiv Machine Learning
Aug 26
Deep Feature Pyramid Convolutional Networks with In-Place Activated Batch Normalization for Automated Skin Lesion Boundary Segmentation
arXiv:1812.00877v2 Announce Type: replace-cross Abstract: Segmentation of skin lesion boundaries in dermoscopic imaging is an important prerequisite step for computer-aided diagnosis of malignant mel...
arXiv AI
Jul 17
A Modern Multimodal Assistant on a 6 GB 2011 GPU: Stage-Validated, All-GPU CUDA Inference for Fermi
arXiv:2607. 14568v1 Announce Type: cross Abstract: A companion study ran a 35B mixture-of-experts model on a 2011 NVIDIA Tesla C2075 (Fermi, sm_20, 6GB) as a GPU-prefill/CPU-decode hybrid, because the 4-bit model did not fit in device memory (arXiv:2606.