Robust Segmentation Models using an Uncertainty Slice Sampling Based Annotation Workflow

• URL: http://arxiv.org/abs/2109.14879v1
• Date: Thu, 30 Sep 2021 06:56:11 GMT
• Title: Robust Segmentation Models using an Uncertainty Slice Sampling Based Annotation Workflow
• Authors: Grzegorz Chlebus and Andrea Schenk and Horst K. Hahn and Bram van Ginneken and Hans Meine
• Abstract summary: We propose an uncertainty slice sampling (USS) strategy for semantic segmentation of 3D medical volumes. We demonstrate the efficiency of USS on a liver segmentation task using multi-site data.
• Score: 5.051373749267151
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semantic segmentation neural networks require pixel-level annotations in large quantities to achieve a good performance. In the medical domain, such annotations are expensive, because they are time-consuming and require expert knowledge. Active learning optimizes the annotation effort by devising strategies to select cases for labeling that are most informative to the model. In this work, we propose an uncertainty slice sampling (USS) strategy for semantic segmentation of 3D medical volumes that selects 2D image slices for annotation and compare it with various other strategies. We demonstrate the efficiency of USS on a CT liver segmentation task using multi-site data. After five iterations, the training data resulting from USS consisted of 2410 slices (4% of all slices in the data pool) compared to 8121 (13%), 8641 (14%), and 3730 (6%) for uncertainty volume (UVS), random volume (RVS), and random slice (RSS) sampling, respectively. Despite being trained on the smallest amount of data, the model based on the USS strategy evaluated on 234 test volumes significantly outperformed models trained according to other strategies and achieved a mean Dice index of 0.964, a relative volume error of 4.2%, a mean surface distance of 1.35 mm, and a Hausdorff distance of 23.4 mm. This was only slightly inferior to 0.967, 3.8%, 1.18 mm, and 22.9 mm achieved by a model trained on all available data, but the robustness analysis using the 5th percentile of Dice and the 95th percentile of the remaining metrics demonstrated that USS resulted not only in the most robust model compared to other sampling schemes, but also outperformed the model trained on all data according to Dice (0.946 vs. 0.945) and mean surface distance (1.92 mm vs. 2.03 mm).

Related papers

• A Novel Adaptive Fine-Tuning Algorithm for Multimodal Models: Self-Optimizing Classification and Selection of High-Quality Datasets in Remote Sensing [46.603157010223505]
  We propose an adaptive fine-tuning algorithm for multimodal large models. We train the model on two 3090 GPU using one-third of the GeoChat multimodal remote sensing dataset. The model achieved scores of 89.86 and 77.19 on the UCMerced and AID evaluation datasets.
  arXiv  Detail & Related papers  (2024-09-20T09:19:46Z)
• Compact Language Models via Pruning and Knowledge Distillation [61.56557874432008]
  Minitron models exhibit up to a 16% improvement in MMLU scores compared to training from scratch. Deriving 8B and 4B models from an already pretrained 15B model using our approach requires up to 40x fewer training tokens per model compared to training from scratch.
  arXiv  Detail & Related papers  (2024-07-19T21:47:57Z)
• DCSM 2.0: Deep Conditional Shape Models for Data Efficient Segmentation [11.532639713283226]
  We introduce Deep Conditional Shape Models 2.0, which uses an edge detector, along with an implicit shape function conditioned on edge maps, to leverage cross-modality shape information. We demonstrate data efficiency in the target domain by varying the amounts of training data used in the edge detection stage. The method scales well to low data regimes, with gains of up to 5% in dice coefficient, 2.58 mm in average surface distance and 21.02 mm in Hausdorff distance when using just 2% (22 volumes) of the training data.
  arXiv  Detail & Related papers  (2024-06-28T18:52:11Z)
• Common 7B Language Models Already Possess Strong Math Capabilities [61.61442513067561]
  This paper shows that the LLaMA-2 7B model with common pre-training already exhibits strong mathematical abilities. The potential for extensive scaling is constrained by the scarcity of publicly available math questions.
  arXiv  Detail & Related papers  (2024-03-07T18:00:40Z)
• A Lightweight and Accurate Face Detection Algorithm Based on Retinaface [0.5076419064097734]
  We propose a lightweight and accurate face detection algorithm LAFD (Light and accurate face detection) based on Retinaface. Backbone network in the algorithm is a modified MobileNetV3 network which adjusts the size of the convolution kernel. If the input image is pre-processed and scaled to 1560px in length or 1200px in width, the model achieves an average accuracy of 86.2%.
  arXiv  Detail & Related papers  (2023-08-08T15:36:57Z)
• An Empirical Study of Large-Scale Data-Driven Full Waveform Inversion [33.19446101601603]
  This paper investigates the impact of big data on deep learning models to help solve the full waveform inversion (FWI) problem. We train and evaluate the FWI models on a combination of 10 2D subsets in OpenFWI that contain 470K pairs of seismic data and velocity maps in total. Our experiments demonstrate that training on the combined dataset yields an average improvement of 13.03% in MAE, 7.19% in MSE and 1.87% in SSIM.
  arXiv  Detail & Related papers  (2023-07-28T08:32:11Z)
• Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning [79.43940012723539]
  ADCLR is a self-supervised learning framework for learning accurate and dense vision representation. Our approach achieves new state-of-the-art performance for contrastive methods.
  arXiv  Detail & Related papers  (2023-06-23T07:38:09Z)
• Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization [17.3513750927719]
  Deep learning models have demonstrated great potential in medical 3D imaging, but their development is limited by the expensive, large volume of annotated data required. Active learning (AL) addresses this by training a model on a subset of the most informative data samples without compromising performance. We compared different AL strategies and propose a framework that minimizes the amount of data needed for state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-02-05T04:45:08Z)
• The case for 4-bit precision: k-bit Inference Scaling Laws [75.4335600212427]
  Quantization methods reduce the number of bits required to represent each parameter in a model. The final model size depends on both the number of parameters of the original model and the rate of compression. We run more than 35,000 zero-shot experiments with 16-bit inputs and k-bit parameters to examine which quantization methods improve scaling for 3 to 8-bit precision.
  arXiv  Detail & Related papers  (2022-12-19T18:48:33Z)
• Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems [63.713297451300086]
  We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B. Their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system.
  arXiv  Detail & Related papers  (2022-06-15T20:44:23Z)
• Improving Deep-learning-based Semi-supervised Audio Tagging with Mixup [2.707154152696381]
  Semi-supervised learning (SSL) methods have been shown to provide state-of-the-art results on image datasets by exploiting unlabeled data. In this article, we adapted four recent SSL methods to the task of audio tagging.
  arXiv  Detail & Related papers  (2021-02-16T14:33:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.