arXiv Machine Learning
Jun 30

CADS: Conformal Adaptive Decision System for Cost-Efficient Image Classification

arXiv:2605. 16401v2 Announce Type: replace-cross Abstract: While high-capacity AI models have advanced state-of-the-art performance, their practical deployment is often hindered by high inference costs, environmental impact, and a "one-size-fits-all" approach that ignores varying sample complexity.

By Mikael Turkoglu, Tim Bary, Vincent Thielens, Manon Dausort, Beno\^it Macq
arXiv Computer Vision
Sep 14

Uni-Light: An Ultra-Lightweight Framework via Uncertainty-Aware Knowledge Distillation for Brain Tumour Segmentation

arXiv:2609.06729v2 Announce Type: replace Abstract: Accurate 3D brain tumour segmentation from multi-modal Magnetic Resonance Imaging (MRI) is essential for clinical diagnosis and treatment planning....

By Libing Kuang, Soren Salehi, Ziling Wu, Ahmad P. Tafti, Armaghan Moemeni
arXiv Computer Vision
Sep 21

Uncertainty-driven training for three-dimensional calibrated lung nodule classification

The paper introduces an uncertainty‑driven training framework for 3D CT lung nodule classification that uses validation‑based uncertainty estimates to reweight the loss, aiming to improve predictive performance and probability calibration. Two uncertainty quantification methods—Monte Carlo Dropout and Evidential Deep Learning—are evaluated across multiple backbone architectures (ResNet, DenseNet, EfficientNet, ViT, Swin) on the LIDC‑IDRI and NoduleMNIST3D datasets. The approach yields comparable classification accuracy to conventional training while substantially reducing expected calibration error, especially on convolutional backbones, and shows that simple temperature scaling can also achieve strong calibration.

By Giuseppe Tripodi, Alessandro De Rosis, Saleh Rezaeiravesh
arXiv Computer Vision
Oct 22

SpineContextResUNet: A Computationally Efficient Residual UNet for Spine CT Segmentation

SpineContextResUNet is a lightweight 3D Residual U‑Net that uses a Context Block with parallel multi‑dilated convolutions to efficiently capture long‑range anatomical dependencies for spine CT segmentation. It achieves Dice scores of 88.17% on VerSe2020 and 88.13% on CTSpine1K while maintaining a small ~1.7 M parameter footprint. Compared to resource‑heavy Transformers and baselines that fail on commodity hardware, SpineContextResUNet delivers robust inference on edge devices such as the Nvidia Jetson Orin Nano.

By K S Nithurshen, Saurabh J. Shigwan