Data Diet: Can Trimming PET/CT Datasets Enhance Lesion Segmentation?

• URL: http://arxiv.org/abs/2409.13548v4
• Date: Fri, 22 Nov 2024 12:43:37 GMT
• Title: Data Diet: Can Trimming PET/CT Datasets Enhance Lesion Segmentation?
• Authors: Alexander Jaus, Simon Reiß, Jens Kleesiek, Rainer Stiefelhagen,
• Abstract summary: We describe our approach to compete in the autoPET3 datacentric track. We find that in the autoPETIII dataset, a model that is trained on the entire dataset exhibits undesirable characteristics. We counteract this by removing the easiest samples from the training dataset as measured by the model loss before retraining from scratch.
• Score: 70.38903555729081
• License:
• Abstract: In this work, we describe our approach to compete in the autoPET3 datacentric track. While conventional wisdom suggests that larger datasets lead to better model performance, recent studies indicate that excluding certain training samples can enhance model accuracy. We find that in the autoPETIII dataset, a model that is trained on the entire dataset exhibits undesirable characteristics by producing a large number of false positives particularly for PSMA-PETs. We counteract this by removing the easiest samples from the training dataset as measured by the model loss before retraining from scratch. Using the proposed approach we manage to drive down the false negative volume and improve upon the baseline model in both false negative volume and dice score on the preliminary test set. Code and pre-trained models are available at github.com/alexanderjaus/autopet3_datadiet.

Related papers

• Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selection [80.85902083005237]
  We introduce Data Debiasing with Datamodels (D3M), a debiasing approach which isolates and removes specific training examples that drive the model's failures on minority groups.
  arXiv  Detail & Related papers  (2024-06-24T17:51:01Z)
• Verifix: Post-Training Correction to Improve Label Noise Robustness with Verified Samples [9.91998873101083]
  Post-Training Correction adjusts model parameters after initial training to mitigate label noise. We introduce Verifix, a novel algorithm that leverages a small, verified dataset to correct the model weights using a single update. Experiments on the CIFAR dataset with 25% synthetic corruption show 7.36% generalization improvements on average.
  arXiv  Detail & Related papers  (2024-03-13T15:32:08Z)
• Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models [115.501751261878]
  Fine-tuning language models(LMs) on human-generated data remains a prevalent practice. We investigate whether we can go beyond human data on tasks where we have access to scalar feedback. We find that ReST$EM$ scales favorably with model size and significantly surpasses fine-tuning only on human data.
  arXiv  Detail & Related papers  (2023-12-11T18:17:43Z)
• Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals [91.59906995214209]
  We propose a new evaluation method, Counterfactual Attentiveness Test (CAT) CAT uses counterfactuals by replacing part of the input with its counterpart from a different example, expecting an attentive model to change its prediction. We show that GPT3 becomes less attentive with an increased number of demonstrations, while its accuracy on the test data improves.
  arXiv  Detail & Related papers  (2023-11-16T06:27:35Z)
• Continual Pre-Training of Large Language Models: How to (re)warm your model? [21.8468835868142]
  Large language models (LLMs) are routinely pre-trained on tokens, only to restart the process over again once new data becomes available. We study the warmup phase of models pretrained on the Pile (upstream data, 300B tokens) as we continue to pre-train on SlimPajama (downstream data, 297B tokens) Our results show that while re-warming models first increases the loss on upstream and downstream data, in the longer run it improves the downstream performance, outperforming models trained from scratch$ billionsx2013$even for a large downstream dataset.
  arXiv  Detail & Related papers  (2023-08-08T03:18:18Z)
• Learning Sample Difficulty from Pre-trained Models for Reliable Prediction [55.77136037458667]
  We propose to utilize large-scale pre-trained models to guide downstream model training with sample difficulty-aware entropy regularization. We simultaneously improve accuracy and uncertainty calibration across challenging benchmarks.
  arXiv  Detail & Related papers  (2023-04-20T07:29:23Z)
• You Only Need a Good Embeddings Extractor to Fix Spurious Correlations [26.23962870932271]
  GroupDRO requires training a model in an end-to-end manner with subgroup labels. We show that we can achieve up to 90% accuracy without using any sub-group information in the training set.
  arXiv  Detail & Related papers  (2022-12-12T21:42:33Z)
• Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics [118.75207687144817]
  We introduce Data Maps, a model-based tool to characterize and diagnose datasets. We leverage a largely ignored source of information: the behavior of the model on individual instances during training. Our results indicate that a shift in focus from quantity to quality of data could lead to robust models and improved out-of-distribution generalization.
  arXiv  Detail & Related papers  (2020-09-22T20:19:41Z)
• A Comparison of LSTM and BERT for Small Corpus [0.0]
  Recent advancements in the NLP field showed that transfer learning helps with achieving state-of-the-art results for new tasks by tuning pre-trained models instead of starting from scratch. In this paper we focus on a real-life scenario that scientists in academia and industry face frequently: given a small dataset, can we use a large pre-trained model like BERT and get better results than simple models? Our experimental results show that bidirectional LSTM models can achieve significantly higher results than a BERT model for a small dataset and these simple models get trained in much less time than tuning the pre-trained counterparts.
  arXiv  Detail & Related papers  (2020-09-11T14:01:14Z)

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.