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Found 11 papers, 7 papers with code
Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models
Multi-modality foundation models, as represented by GPT-4V, have brought a new paradigm for low-level visual perception and understanding tasks, that can respond to a broad range of natural human instructions in a model.
Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural Networks
On ImageNet, we achieve up to 4. 7% and 4. 6% higher top-1 accuracy compared to other methods for VGG-16 and ResNet-50, respectively.
GaitFormer: Revisiting Intrinsic Periodicity for Gait Recognition
The second component is the Temporal Aggregation Module (TAM), which separates embeddings into trend and seasonal components, and extracts meaningful temporal correlations to identify primary components, while filtering out random noise.
MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation
Our method demonstrates promising segmentation performance with a mean Dice score of 83. 8% and 81. 4% and an average asymmetric surface distance (ASSD) of 0. 55 mm and 0. 26 mm for the VS and Cochlea, respectively in the validation phase of the crossMoDA 2022 challenge.
MetaGrad: Adaptive Gradient Quantization with Hypernetworks
A popular track of network compression approach is Quantization aware Training (QAT), which accelerates the forward pass during the neural network training and inference.
LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation
To assess the effectiveness of our method, we conduct extensive experiments on two different tasks for cross-modality segmentation between MRI and CT images.
Self-supervised Assisted Active Learning for Skin Lesion Segmentation
Label scarcity has been a long-standing issue for biomedical image segmentation, due to high annotation costs and professional requirements.
MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels
Although deep unsupervised domain adaptation (UDA) can leverage well-established source domain annotations and abundant target domain data to facilitate cross-modality image segmentation and also mitigate the label paucity problem on the target domain, the conventional UDA methods suffer from severe performance degradation when source domain annotations are scarce.
Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation
Deep learning has achieved promising segmentation performance on 3D left atrium MR images.
DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation
We use the proposed criteria to select samples for strong and weak labelers to produce oracle labels and pseudo labels simultaneously at each active learning iteration in an ensemble learning manner, which can be examined with IoMT Platform.
Multi-Instance Multi-Label Learning for Gene Mutation Prediction in Hepatocellular Carcinoma
Gene mutation prediction in hepatocellular carcinoma (HCC) is of great diagnostic and prognostic value for personalized treatments and precision medicine.
