Data augmentation for deep ensembles in polyp segmentation

Nanni, Loris; Cuza, Daniela; Lumini, Alessandra; Brahnam, Sheryl

doi:10.1088/978-0-7503-4821-8ch8

Cited by 9 publications

(9 citation statements)

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“…They emulate the human brain's visual perception [25]. Using these models in ensembles has demonstrated advantages in terms of enhanced performance (see for instance, [12], [43], [44], [48]).…”

Section: A Convolutional Neural Networkmentioning

confidence: 99%

An Enhanced Loss Function for Semantic Road Segmentation in Remote Sensing Images

Nanni,

Brahnam,

Loreggia

2024

IEEE Access

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The analysis of road continuity in satellite images is a complex challenge. This is due to the difficulty in identifying the directional vector of road sections, especially when the satellite view of roads is obstructed by trees or other structures. Today, most research focuses on optimizing the deep learning network topology, however, the accuracy of segmentation is affected by the loss function used in training; currently, little research has been published on ad-hoc loss functions for road segmentation.To solve this problem, we proposed loss functions based on topological pixel analysis, in which more weight is given to problematic pixels representing non-real road breaks. We report the results of different tests, obtaining state-of-the-art performance among convolution neural network based approaches. Both the code and information for replicating our experiments are available on https://github.com/LorisNanni/An-Enhanced-Loss-Function-for-Semantic-Road-Segmentation-in-Remote-Sensing-Images , so as to enable future reliable comparisons.

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Section: A Convolutional Neural Networkmentioning

confidence: 99%

An Enhanced Loss Function for Semantic Road Segmentation in Remote Sensing Images

Nanni,

Brahnam,

Loreggia

2024

IEEE Access

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“…Nanni et al. [63] leveraged on differences of various classifiers by an ensemble of CNNs with different methods.…”

Section: Related Workmentioning

confidence: 99%

Parallel matters: Efficient polyp segmentation with parallel structured feature augmentation modules

Guo

Fang

Wang

et al. 2023

IET Image Processing

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The large variations of polyp sizes and shapes and the close resemblances of polyps to their surroundings call for features with long-range information in rich scales and strong discrimination. This article proposes two parallel structured modules for building those features. One is the Transformer Inception module (TI) which applies Transformers with different reception fields in parallel to input features and thus enriches them with more long-range information in more scales. The other is the Local-Detail Augmentation module (LDA) which applies the spatial and channel attentions in parallel to each block and thus locally augments the features from two complementary dimensions for more object details. Integrating TI and LDA, a new Transformer encoder based framework, Parallel-Enhanced Network (PENet), is proposed, where LDA is specifically adopted twice in a coarse-tofine way for accurate prediction. PENet is efficient in segmenting polyps with different sizes and shapes without the interference from the background tissues. Experimental comparisons with state-of-the-arts methods show its merits.

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“…Four [ 27 , 28 , 29 , 30 ] have been superseded by newer publications from the same authors, which show better performance on a wider range of datasets.…”

Section: Related Workmentioning

confidence: 99%

Ensembles of Convolutional Neural Networks and Transformers for Polyp Segmentation

Nanni

Fantozzi

Loreggia

et al. 2023

Sensors

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In the realm of computer vision, semantic segmentation is the task of recognizing objects in images at the pixel level. This is done by performing a classification of each pixel. The task is complex and requires sophisticated skills and knowledge about the context to identify objects’ boundaries. The importance of semantic segmentation in many domains is undisputed. In medical diagnostics, it simplifies the early detection of pathologies, thus mitigating the possible consequences. In this work, we provide a review of the literature on deep ensemble learning models for polyp segmentation and develop new ensembles based on convolutional neural networks and transformers. The development of an effective ensemble entails ensuring diversity between its components. To this end, we combined different models (HarDNet-MSEG, Polyp-PVT, and HSNet) trained with different data augmentation techniques, optimization methods, and learning rates, which we experimentally demonstrate to be useful to form a better ensemble. Most importantly, we introduce a new method to obtain the segmentation mask by averaging intermediate masks after the sigmoid layer. In our extensive experimental evaluation, the average performance of the proposed ensembles over five prominent datasets beat any other solution that we know of. Furthermore, the ensembles also performed better than the state-of-the-art on two of the five datasets, when individually considered, without having been specifically trained for them.

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Data augmentation for deep ensembles in polyp segmentation

Cited by 9 publications

References 0 publications

An Enhanced Loss Function for Semantic Road Segmentation in Remote Sensing Images

An Enhanced Loss Function for Semantic Road Segmentation in Remote Sensing Images

Parallel matters: Efficient polyp segmentation with parallel structured feature augmentation modules

Ensembles of Convolutional Neural Networks and Transformers for Polyp Segmentation

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