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Found 10 papers, 6 papers with code
FedDRO: Federated Compositional Optimization for Distributionally Robust Learning
A key novelty of our work is to develop solution accuracy-independent algorithms that do not require large batch gradients (and function evaluations) for solving federated CO problems.
GeoSAM: Fine-tuning SAM with Sparse and Dense Visual Prompting for Automated Segmentation of Mobility Infrastructure
The Segment Anything Model (SAM) has shown impressive performance when applied to natural image segmentation.
Interpretability-Aware Vision Transformer
Furthermore, if ViTs are not properly trained with the given data and do not prioritize the region of interest, the {\it post hoc} methods would be less effective.
Auto-Prompting SAM for Mobile Friendly 3D Medical Image Segmentation
In addition to the domain gaps between natural and medical images, disparities in the spatial arrangement between 2D and 3D images, the substantial computational burden imposed by powerful GPU servers, and the time-consuming manual prompt generation impede the extension of SAM to a broader spectrum of medical image segmentation applications.
Fairness-aware Vision Transformer via Debiased Self-Attention
Importantly, our DSA framework leads to improved fairness guarantees over prior works on multiple prediction tasks without compromising target prediction performance.
Negative Flux Aggregation to Estimate Feature Attributions
There are increasing demands for understanding deep neural networks' (DNNs) behavior spurred by growing security and/or transparency concerns.
Coupling User Preference with External Rewards to Enable Driver-centered and Resource-aware EV Charging Recommendation
Electric Vehicle (EV) charging recommendation that both accommodates user preference and adapts to the ever-changing external environment arises as a cost-effective strategy to alleviate the range anxiety of private EV drivers.
FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT Images
Computed Tomography (CT) based precise prostate segmentation for treatment planning is challenging due to (1) the unclear boundary of the prostate derived from CT's poor soft tissue contrast and (2) the limitation of convolutional neural network-based models in capturing long-range global context.
Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System
We hypothesize that adversarial training can eliminate shortcut features whereas saliency guided training can filter out non-relevant features; both are nuisance features accounting for the performance degradation on OOD test sets.
COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays
We design and implement a novel three-player knowledge transfer and distillation (KTD) framework including a pre-trained attending physician (AP) network that extracts CXR imaging features from a large scale of lung disease CXR images, a fine-tuned resident fellow (RF) network that learns the essential CXR imaging features to discriminate COVID-19 from pneumonia and/or normal cases with a small amount of COVID-19 cases, and a trained lightweight medical student (MS) network to perform on-device COVID-19 patient triage and follow-up.
