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Found 10 papers, 3 papers with code
Sparsity- and Hybridity-Inspired Visual Parameter-Efficient Fine-Tuning for Medical Diagnosis
The success of Large Vision Models (LVMs) is accompanied by vast data volumes, which are prohibitively expensive in medical diagnosis. To address this, recent efforts exploit Parameter-Efficient Fine-Tuning (PEFT), which trains a small number of weights while freezing the rest. However, they typically assign trainable weights to the same positions in LVMs in a heuristic manner, regardless of task differences, making them suboptimal for professional applications like medical diagnosis. To address this, we statistically reveal the nature of sparsity and hybridity during diagnostic-targeted fine-tuning, i. e., a small portion of key weights significantly impacts performance, and these key weights are hybrid, including both task-specific and task-agnostic parts. Based on this, we propose a novel Sparsity- and Hybridity-inspired Parameter Efficient Fine-Tuning (SH-PEFT). It selects and trains a small portion of weights based on their importance, which is innovatively estimated by hybridizing both task-specific and task-agnostic strategies. Validated on six medical datasets of different modalities, we demonstrate that SH-PEFT achieves state-of-the-art performance in transferring LVMs to medical diagnosis in terms of accuracy.
Learning Large Margin Sparse Embeddings for Open Set Medical Diagnosis
To tackle OSR, we assume that known classes could densely occupy small parts of the embedding space and the remaining sparse regions could be recognized as unknowns.
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation
Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches.
Parallel Network with Channel Attention and Post-Processing for Carotid Arteries Vulnerable Plaque Segmentation in Ultrasound Images
Test results show that the proposed method with dice loss function yields a Dice value of 0. 820, an IoU of 0. 701, Acc of 0. 969, and modified Hausdorff distance (MHD) of 1. 43 for 30 vulnerable cases of plaques, it outperforms some of the conventional CNN-based methods on these metrics.
Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation
In this paper, we propose a novel uncertainty-guided mutual consistency learning framework to effectively exploit unlabeled data by integrating intra-task consistency learning from up-to-date predictions for self-ensembling and cross-task consistency learning from task-level regularization to exploit geometric shape information.
Parameter Decoupling Strategy for Semi-supervised 3D Left Atrium Segmentation
Based on this, the feature extractor is constrained to encourage the consistency of probability maps generated by classifiers under diversified features.
Dual-Task Mutual Learning for Semi-Supervised Medical Image Segmentation
The success of deep learning methods in medical image segmentation tasks usually requires a large amount of labeled data.
Exploiting Shared Knowledge from Non-COVID Lesions for Annotation-Efficient COVID-19 CT Lung Infection Segmentation
In this paper, we propose a novel relation-driven collaborative learning model to exploit shared knowledge from non-COVID lesions for annotation-efficient COVID-19 CT lung infection segmentation.
Bridging 2D and 3D Segmentation Networks for Computation Efficient Volumetric Medical Image Segmentation: An Empirical Study of 2.5D Solutions
Despite these works lead to improvements on a variety of segmentation tasks, to the best of our knowledge, there has not previously been a large-scale empirical comparison of these methods.
SAU-Net: Efficient 3D Spine MRI Segmentation Using Inter-Slice Attention
Accurate segmentation of spine Magnetic Resonance Imaging (MRI) is highly demanded in morphological research, quantitative analysis, and diseases identification, such as spinal canal stenosis, disc herniation and degeneration.
