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Found 11 papers, 7 papers with code
PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images
This challenge aimed to enhance the development of automatic segmentation algorithms at an international scale, providing the largest dataset to date with 5, 101 intrapartum ultrasound images collected from two ultrasound machines across three hospitals from two institutions.
CoMoTo: Unpaired Cross-Modal Lesion Distillation Improves Breast Lesion Detection in Tomosynthesis
Our framework leverages unpaired mammography data to enhance the training of a DBT model, improving practicality by eliminating the need for mammography during inference.
Learning Unlabeled Clients Divergence for Federated Semi-Supervised Learning via Anchor Model Aggregation
Existing FedSemi methods typically fail to aggregate models from unlabeled clients due to their inherent unreliability, thus overlooking unique information from their heterogeneous data distribution, leading to sub-optimal results.
FD-SOS: Vision-Language Open-Set Detectors for Bone Fenestration and Dehiscence Detection from Intraoral Images
Accurate detection of bone fenestration and dehiscence (FD) is crucial for effective treatment planning in dentistry.
Evaluating the Fairness of Neural Collapse in Medical Image Classification
Through extensive experiments on three medical imaging datasets -- PAPILA, HAM10000, and CheXpert -- we find that in biased settings, NC can lead to a significant drop in F1 score across all subgroups.
An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image Synthesis
Generative modeling seeks to approximate the statistical properties of real data, enabling synthesis of new data that closely resembles the original distribution.
Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation
Recent advancements in large foundation models have shown promising potential in the medical industry due to their flexible prompting capability.
Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification
Existing federated methods under highly imbalanced datasets primarily focus on optimizing a global model without incorporating the intra-class variations that can arise in medical imaging due to different populations, findings, and scanners.
FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition
Representational transfer from publicly available models is a promising technique for improving medical image classification, especially in long-tailed datasets with rare diseases.
Seamless Iterative Semi-Supervised Correction of Imperfect Labels in Microscopy Images
We propose Seamless Iterative Semi-Supervised correction of Imperfect labels (SISSI), a new method for training object detection models with noisy and missing annotations in a semi-supervised fashion.
Mammograms Classification: A Review
An advanced reliable low-cost form of screening method, Digital mammography has been used as an effective imaging method for breast cancer detection.
