Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
RankSEG: A Consistent Ranking-based Framework for Segmentation
Authors: Ben Dai, Chunlin Li
JMLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we establish a theoretical foundation of segmentation with respect to the Dice/Io U metrics, including the Bayes rule and Dice-/Io U-calibration, analogous to classification-calibration or Fisher consistency in classification. ... The numerical effectiveness of Rank Dice/m Rank Dice is demonstrated in various simulated examples and Fine-annotated City Scapes, Pascal VOC and Kvasir-SEG datasets with state-of-the-art deep learning architectures. |
| Researcher Affiliation | Academia | Ben Dai EMAIL Department of Statistics The Chinese University of Hong Kong Hong Kong SAR. Chunlin Li EMAIL School of Statistics University of Minnesota MN 55455 USA. |
| Pseudocode | Yes | Algorithm 1: Computing schemes for the proposed Rank Dice framework. ... Algorithm 2: m Rank Dice for overlapping m Dice-segmentation. |
| Open Source Code | Yes | Python module and source code are available on GITHUB at https://github.com/statmlben/rankseg. |
| Open Datasets | Yes | The numerical effectiveness of Rank Dice/m Rank Dice is demonstrated in various simulated examples and Fine-annotated City Scapes, Pascal VOC and Kvasir-SEG datasets with state-of-the-art deep learning architectures. |
| Dataset Splits | Yes | Pascal VOC 2012 dataset contains 1,464 training and 1,449 validation pixel-level annotated images. |
| Hardware Specification | Yes | All experiments are conducted using Py Torch and CUDA on an NVIDIA Ge Force RTX 3080 GPU. |
| Software Dependencies | No | All experiments are conducted using Py Torch and CUDA on an NVIDIA Ge Force RTX 3080 GPU. ... The experiment protocol of our numerical sections basically follows a well-developed Github repository PYTORCH-SEGMENTATION (Ouali, 2022). |
| Experiment Setup | Yes | For all methods, we employ SGD on the learning rate (lr) schedule lr schedule= poly , and the initial learning rate initial lr=0.01, weight decay=100, momentum=0.9, crop size 512x512, batch size 6, and 300 epochs. The performance on validation set is measured in terms of the m Dice and m Io U averaged across 19 object classes (Table 2). |