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Found 17 papers, 5 papers with code
Showing LLM-Generated Code Selectively Based on Confidence of LLMs
HonestCoder selectively shows the generated programs to developers based on LLMs' confidence.
Segmenting Medical Images: From UNet to Res-UNet and nnUNet
nnUNet was unmatched in myocardium segmentation, achieving top scores in precision, recall, DSC, and IoU.
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories
Our experiments reveal these LLMs' coding abilities in real-world code repositories.
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms.
DevEval: Evaluating Code Generation in Practical Software Projects
Compared to previous benchmarks, DevEval aligns to practical projects in multiple dimensions, e. g., real program distributions, sufficient dependencies, and enough-scale project contexts.
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis.
FashionFlow: Leveraging Diffusion Models for Dynamic Fashion Video Synthesis from Static Imagery
Our study introduces a new image-to-video generator called FashionFlow to generate fashion videos.
Performance Analysis of UNet and Variants for Medical Image Segmentation
Medical imaging plays a crucial role in modern healthcare by providing non-invasive visualisation of internal structures and abnormalities, enabling early disease detection, accurate diagnosis, and treatment planning.
EditSum: A Retrieve-and-Edit Framework for Source Code Summarization
Besides the patternized words, a code summary also contains important keywords, which are the key to reflecting the functionality of the code.
Deep learning for Background Replacement in Video Conferencing
Background replacement is one of the most used features in video conferencing applications by many people, perhaps mainly for privacy protection, but also for other purposes such as branding, marketing and promoting professionalism.
Skin Lesion Diagnosis Using Convolutional Neural Networks
The models were designed with two main purposes.
Structured Chain-of-Thought Prompting for Code Generation
In this paper, we propose Structured CoTs (SCoTs) and present a novel prompting technique for code generation, named SCoT prompting.
AceCoder: Utilizing Existing Code to Enhance Code Generation
A key question is how to make prompts (i. e., Prompting Techniques).
Segmentation of Retinal Blood Vessels Using Deep Learning
The morphology of retinal blood vessels can indicate various diseases in the human body, and researchers have been working on automatic scanning and segmentation of retinal images to aid diagnosis.
Retinal Image Segmentation with Small Datasets
Many eye diseases like Diabetic Macular Edema (DME), Age-related Macular Degeneration (AMD), and Glaucoma manifest in the retina, can cause irreversible blindness or severely impair the central version.
nnUNet RASPP for Retinal OCT Fluid Detection, Segmentation and Generalisation over Variations of Data Sources
Retinal Optical Coherence Tomography (OCT), a noninvasive cross-sectional scan of the eye with qualitative 3D visualization of the retinal anatomy is use to study the retinal structure and the presence of pathogens.
Retrieve and Refine: Exemplar-based Neural Comment Generation
Inspired by the IR-based and template-based approaches, in this paper, we propose a neural comment generation approach where we use the existing comments of similar code snippets as exemplars to guide comment generation.
