Search Results for author:
Found 10 papers, 3 papers with code
HAWP: a Dataset for Hindi Arithmetic Word Problem Solving
Recently there has been a surge in this area of word problem solving in Chinese with the creation of large benchmark datastes.
MAIRA-2: Grounded Radiology Report Generation
Radiology reporting is a complex task that requires detailed image understanding, integration of multiple inputs, including comparison with prior imaging, and precise language generation.
Challenges for Responsible AI Design and Workflow Integration in Healthcare: A Case Study of Automatic Feeding Tube Qualification in Radiology
Nasogastric tubes (NGTs) are feeding tubes that are inserted through the nose into the stomach to deliver nutrition or medication.
RAD-DINO: Exploring Scalable Medical Image Encoders Beyond Text Supervision
We introduce RAD-DINO, a biomedical image encoder pre-trained solely on unimodal biomedical imaging data that obtains similar or greater performance than state-of-the-art biomedical language supervised models on a diverse range of benchmarks.
RadEdit: stress-testing biomedical vision models via diffusion image editing
Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing.
Verb Categorisation for Hindi Word Problem Solving
Verbs are very important for solving word problems with addition/subtraction operations as they help us identify the set of operations required to solve the word problems.
Exploring the Boundaries of GPT-4 in Radiology
In this paper, we focus on assessing the performance of GPT-4, the most capable LLM so far, on the text-based applications for radiology reports, comparing against state-of-the-art (SOTA) radiology-specific models.
Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing
Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images.
Discovering Salient Anatomical Landmarks by Predicting Human Gaze
In this paper, in contrast, we present a method to automatically discover and localize anatomical landmarks in medical images.
Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention
Secondly, we train a simple softmax regression on the feature activations of each CNN layer in order to evaluate the representations independently of transfer learning hyper-parameters.
