MLC at HECKTOR 2022: The Effect and Importance of Training Data when Analyzing Cases of Head and Neck Tumors using Machine Learning

• URL: http://arxiv.org/abs/2211.16834v1
• Date: Wed, 30 Nov 2022 09:04:27 GMT
• Title: MLC at HECKTOR 2022: The Effect and Importance of Training Data when Analyzing Cases of Head and Neck Tumors using Machine Learning
• Authors: Vajira Thambawita, Andrea M. Stor{\aa}s, Steven A. Hicks, P{\aa}l Halvorsen, Michael A. Riegler
• Abstract summary: This paper presents the work done by team MLC for the 2022 version of the HECKTOR grand challenge held at MICCAI 2022. Analysis of Positron Emission Tomography (PET) and Computed Tomography (CT) images has been proposed to identify patients with a prognosis.
• Score: 0.9166327220922845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Head and neck cancers are the fifth most common cancer worldwide, and recently, analysis of Positron Emission Tomography (PET) and Computed Tomography (CT) images has been proposed to identify patients with a prognosis. Even though the results look promising, more research is needed to further validate and improve the results. This paper presents the work done by team MLC for the 2022 version of the HECKTOR grand challenge held at MICCAI 2022. For Task 1, the automatic segmentation task, our approach was, in contrast to earlier solutions using 3D segmentation, to keep it as simple as possible using a 2D model, analyzing every slice as a standalone image. In addition, we were interested in understanding how different modalities influence the results. We proposed two approaches; one using only the CT scans to make predictions and another using a combination of the CT and PET scans. For Task 2, the prediction of recurrence-free survival, we first proposed two approaches, one where we only use patient data and one where we combined the patient data with segmentations from the image model. For the prediction of the first two approaches, we used Random Forest. In our third approach, we combined patient data and image data using XGBoost. Low kidney function might worsen cancer prognosis. In this approach, we therefore estimated the kidney function of the patients and included it as a feature. Overall, we conclude that our simple methods were not able to compete with the highest-ranking submissions, but we still obtained reasonably good scores. We also got interesting insights into how the combination of different modalities can influence the segmentation and predictions.

Related papers

• Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images [45.29301790646322]
  Computer-aided diagnosis can help with early lung nodul detection and facilitate subsequent nodule characterization. We propose CADe, for segmenting lung nodules in a zero-shot manner using a variant of the Segment Anything Model called MedSAM. We also propose, CADx, a method for the nodule characterization as benign/malignant by making a gallery of radiomic features and aligning image-feature pairs through contrastive learning.
  arXiv  Detail & Related papers  (2024-07-02T19:30:25Z)
• Towards Tumour Graph Learning for Survival Prediction in Head & Neck Cancer Patients [0.0]
  Nearly one million new cases of head & neck cancer diagnosed worldwide in 2020. automated segmentation and prognosis estimation approaches can help ensure each patient gets the most effective treatment. This paper presents a framework to perform these functions on arbitrary field of view (FoV) PET and CT registered scans.
  arXiv  Detail & Related papers  (2023-04-17T09:32:06Z)
• TMSS: An End-to-End Transformer-based Multimodal Network for Segmentation and Survival Prediction [0.0]
  oncologists do not do this in their analysis but rather fuse the information in their brain from multiple sources such as medical images and patient history. This work proposes a deep learning method that mimics oncologists' analytical behavior when quantifying cancer and estimating patient survival.
  arXiv  Detail & Related papers  (2022-09-12T06:22:05Z)
• A Novel Mask R-CNN Model to Segment Heterogeneous Brain Tumors through Image Subtraction [0.0]
  We propose using a method performed by radiologists called image segmentation and applying it to machine learning models to prove a better segmentation. Using Mask R-CNN, its ResNet backbone being pre-trained on the RSNA pneumonia detection challenge dataset, we can train a model on the Brats 2020 Brain Tumor dataset. We can see how well the method of image subtraction works by comparing it to models without image subtraction through DICE coefficient (F1 score), recall, and precision on the untouched test set.
  arXiv  Detail & Related papers  (2022-04-04T01:45:11Z)
• Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Auto-Encoders [65.95959936242993]
  We develop a deep learning framework that can accurately predict and visualize the progression of osteolytic bone lesions. It will assist in planning and evaluating treatment strategies to prevent skeletal related events (SREs) in breast cancer patients.
  arXiv  Detail & Related papers  (2022-03-20T21:00:10Z)
• Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling [1.0965065178451103]
  We process two public datasets to set up a benchmark cohort of 341 patient in total for studying outcome prediction of metastatic cancer. We propose two injective multiple instance pooling functions that are better suited to outcome prediction. Our results show that multiple instance learning with injective pooling functions can achieve state-of-the-art performance in the non-small-cell lung cancer CT and head and neck CT outcome prediction benchmarking tasks.
  arXiv  Detail & Related papers  (2022-03-09T16:58:03Z)
• An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using Multimodal Data [0.0]
  We propose a multimodal network that ensembles deep multi-task logistic regression (MTLR), Cox proportional hazard (CoxPH) and CNN models to predict prognostic outcomes for patients with head and neck tumors. Our proposed ensemble solution achieves a C-index of 0.72 on The HECKTOR test set that saved us the first place in prognosis task of the HECKTOR challenge.
  arXiv  Detail & Related papers  (2022-02-25T07:50:59Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Convolutional-LSTM for Multi-Image to Single Output Medical Prediction [55.41644538483948]
  A common scenario in developing countries is to have the volume metadata lost due multiple reasons. It is possible to get a multi-image to single diagnostic model which mimics human doctor diagnostic process.
  arXiv  Detail & Related papers  (2020-10-20T04:30:09Z)
• VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images [121.31355003451152]
  Large Scale Vertebrae Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020. We present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view.
  arXiv  Detail & Related papers  (2020-01-24T21:09:18Z)

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.