One Shot GANs for Long Tail Problem in Skin Lesion Dataset using novel content space assessment metric

• URL: http://arxiv.org/abs/2409.19945v1
• Date: Mon, 30 Sep 2024 04:51:54 GMT
• Title: One Shot GANs for Long Tail Problem in Skin Lesion Dataset using novel content space assessment metric
• Authors: Kunal Deo, Deval Mehta, Kshitij Jadhav,
• Abstract summary: Long tail problems frequently arise in the medical field due to the scarcity of medical data for rare conditions. One Shot GANs was employed to augment the tail class of HAM10000 dataset by generating additional samples.
• Score: 1.833650794546064
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long tail problems frequently arise in the medical field, particularly due to the scarcity of medical data for rare conditions. This scarcity often leads to models overfitting on such limited samples. Consequently, when training models on datasets with heavily skewed classes, where the number of samples varies significantly, a problem emerges. Training on such imbalanced datasets can result in selective detection, where a model accurately identifies images belonging to the majority classes but disregards those from minority classes. This causes the model to lack generalizability, preventing its use on newer data. This poses a significant challenge in developing image detection and diagnosis models for medical image datasets. To address this challenge, the One Shot GANs model was employed to augment the tail class of HAM10000 dataset by generating additional samples. Furthermore, to enhance accuracy, a novel metric tailored to suit One Shot GANs was utilized.

Related papers

• LoGex: Improved tail detection of extremely rare histopathology classes via guided diffusion [36.56346240815833]
  In realistic medical settings, the data are often long-tailed, with most samples concentrated in a few classes and a long tail of rare classes, usually containing just a few samples. This distribution presents a significant challenge because rare conditions are critical to detect and difficult to classify due to limited data. In this paper, rather than attempting to classify rare classes, we aim to detect these as out-of-distribution data reliably.
  arXiv  Detail & Related papers  (2024-09-02T15:18:15Z)
• Adapting Visual-Language Models for Generalizable Anomaly Detection in Medical Images [68.42215385041114]
  This paper introduces a novel lightweight multi-level adaptation and comparison framework to repurpose the CLIP model for medical anomaly detection. Our approach integrates multiple residual adapters into the pre-trained visual encoder, enabling a stepwise enhancement of visual features across different levels. Our experiments on medical anomaly detection benchmarks demonstrate that our method significantly surpasses current state-of-the-art models.
  arXiv  Detail & Related papers  (2024-03-19T09:28:19Z)
• How Can We Tame the Long-Tail of Chest X-ray Datasets? [0.0]
  Chest X-rays (CXRs) are a medical imaging modality that is used to infer a large number of abnormalities. Few of them are quite commonly observed and are abundantly represented in CXR datasets. It is challenging for current models to learn independent discriminatory features for labels that are rare but may be of high significance.
  arXiv  Detail & Related papers  (2023-09-08T12:28:40Z)
• Prevention is better than cure: a case study of the abnormalities detection in the chest [4.000351859705655]
  We show how a series of simple tests for data imbalance exposes faults in the data acquisition and annotation process. Errors made at the data collection stage make it difficult to validate the model correctly. We show how to monitor data and model balance (fairness) throughout the life cycle of a predictive model.
  arXiv  Detail & Related papers  (2023-05-18T13:28:00Z)
• Class-Balancing Diffusion Models [57.38599989220613]
  Class-Balancing Diffusion Models (CBDM) are trained with a distribution adjustment regularizer as a solution. Our method benchmarked the generation results on CIFAR100/CIFAR100LT dataset and shows outstanding performance on the downstream recognition task.
  arXiv  Detail & Related papers  (2023-04-30T20:00:14Z)
• Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model Ensembles [52.77024349608834]
  We analyze the influence of replacing a DCNN with a state-of-the-art face recognition approach, iResNet with ArcFace. Our proposed ensemble model achieves state-of-the-art performance on both seen and unseen disorders.
  arXiv  Detail & Related papers  (2022-11-12T23:28:54Z)
• Covid-19 Detection from Chest X-ray and Patient Metadata using Graph Convolutional Neural Networks [6.420262246029286]
  We propose a novel Graph Convolution Neural Network (GCN) that is capable of identifying bio-markers of Covid-19 pneumonia. The proposed method exploits important relational knowledge between data instances and their features using graph representation and applies convolution to learn the graph data.
  arXiv  Detail & Related papers  (2021-05-20T13:13:29Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Single Model Deep Learning on Imbalanced Small Datasets for Skin Lesion Classification [5.642359877598896]
  This paper proposes a novel data augmentation strategy for single model classification of skin lesions based on a small and imbalanced dataset. Various DCNNs are trained on this dataset to show that the models with moderate complexity outperform the larger models. By combining Modified RandAugment and Multi-weighted Focal Loss in a single DCNN model, we have achieved the classification accuracy comparable to those of multiple ensembling models on the ISIC 2018 challenge test dataset.
  arXiv  Detail & Related papers  (2021-02-02T03:48:55Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification [80.00316465793702]
  We present a deep learning framework that enables robust modeling in challenging scenarios. Our results show that using 85% lesser labeled data, we can build predictive models that match the performance of classifiers trained in a large-scale data setting.
  arXiv  Detail & Related papers  (2020-05-03T02:36:00Z)

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.