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Found 6 papers, 3 papers with code
How Does Pruning Impact Long-Tailed Multi-Label Medical Image Classifiers?
This work represents a first step toward understanding the impact of pruning on model behavior in deep long-tailed, multi-label medical image classification.
Improving Segmentation and Detection of Lesions in CT Scans Using Intensity Distribution Supervision
We propose a method to incorporate the intensity information of a target lesion on CT scans in training segmentation and detection networks.
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study
Imaging exams, such as chest radiography, will yield a small set of common findings and a much larger set of uncommon findings.
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Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor
Our method can be one option for explicitly incorporating intensity distribution information of a target in network training.
Universal Lymph Node Detection in T2 MRI using Neural Networks
Results: Experiments on 122 test T2 MRI volumes revealed that VFNet achieved a 51. 1% mAP and 78. 7% recall at 4 false positives (FP) per volume, while the one-stage model ensemble achieved a mAP of 52. 3% and sensitivity of 78. 7% at 4FP.
Lymph Node Detection in T2 MRI with Transformers
Identification of lymph nodes (LN) in T2 Magnetic Resonance Imaging (MRI) is an important step performed by radiologists during the assessment of lymphoproliferative diseases.
