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Decision forest, including RandomForest, XGBoost, and Light-GBM, dominates the machine learning tasks over tabular data. Recently, several frameworks were developed for decision forest inference, such as ONNX, TreeLite from Amazon, TensorFlow Decision Forest from Google, HummingBirdfrom Microsoft, Nvidia FIL, and lleaves. While these frameworks are fully optimized for inference computations, they are all decoupled with databases and general data management frameworks, which leads to cross-system performance overheads. We first provided a DICT model to understand the performance gaps between decoupled and in-database inference. We further identified that for in-database inference, in addition to the popular UDF-centric representation that encapsulates the ML into one User Defined Function(UDF), there also exists a relation-centric representation that breaks down the decision forest inference into several fine-grained SQL operations. The relation-centric representation can achieve significantly better performance for large models. We optimized both implementations and conducted a comprehensive benchmark to compare these two implementations to the aforementioned decoupled inference pipelines and existing in-database inference pipelines such as Spark-SQL and PostgresML. The evaluation results validated the DICT model and demonstrated the superior performance of our in-database inference design compared to the baselines.
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End-to-End Evidential-Efficient Net for Radiomics Analysis of Brain MRI to Predict Oncogene Expression and Overall Survival
We presented a novel radiomics approach using multimodality MRI to predict the expression of an oncogene (O6-Methylguanine-DNA methyltransferase, MGMT) and overall survival (OS) of glioblastoma (GBM) patients. Specifically, we employed an EffNetV2-T, which was down scaled and modified from EfficientNetV2, as the feature extractor. Besides, we used evidential layers based to control the distribution of prediction outputs. The evidential layers help to classify the high-dimensional radiomics features to predict the methylation status of MGMT and OS. Tests showed that our model achieved an accuracy of 0.844, making it possible to use as a clinic-enabling technique in the diagnosing and management of GBM. Comparison results indicated that our method performed better than existing work.
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- Award ID(s):
- 2115095
- PAR ID:
- 10496627
- Editor(s):
- Wang, Linwei; Dou, Qi; Fletcher, P. Thomas; Speidel, Stefanie; Li, Shuo
- Publisher / Repository:
- Springer Nature Switzerland
- Date Published:
- Journal Name:
- Medical Image Computing and Computer Assisted Intervention -- MICCAI 2022
- ISBN:
- 978-3-031-16437-8
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Proceeding:
- https://doi.org/10.1007/978-3-031-16437-8_27
