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Found 5 papers, 2 papers with code
Meta-learning Pathologies from Radiology Reports using Variance Aware Prototypical Networks
Large pretrained Transformer-based language models like BERT and GPT have changed the landscape of Natural Language Processing (NLP).
Explaining the Effectiveness of Multi-Task Learning for Efficient Knowledge Extraction from Spine MRI Reports
Pretrained Transformer based models finetuned on domain specific corpora have changed the landscape of NLP.
Transfer Learning with intelligent training data selection for prediction of Alzheimer's Disease
In this paper, we attempt solving these issues with transfer learning, where the state-of-the-art VGG architecture is initialized with pre-trained weights from large benchmark datasets consisting of natural images.
Machine Learning on Biomedical Images: Interactive Learning, Transfer Learning, Class Imbalance, and Beyond
In this paper, we highlight three issues that limit performance of machine learning on biomedical images, and tackle them through 3 case studies: 1) Interactive Machine Learning (IML): we show how IML can drastically improve exploration time and quality of direct volume rendering.
A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation
We propose a generalized focal loss function based on the Tversky index to address the issue of data imbalance in medical image segmentation.
Ranked #1 on
Lesion Segmentation
on BUS 2017 Dataset B
