Lung and Colon Cancer Histopathological Image Dataset (LC25000)

Borkowski, Andrew A.; Bui, Marilyn M.; Thomas, L. Brannon; Wilson, Catherine P.; DeLand, Lauren A.; Mastorides, Stephen M.

doi:10.48550/arxiv.1912.12142

Cited by 43 publications

(54 citation statements)

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“…We evaluate our proposed PDBL in the following two datasets Kather Multiclass Dataset [11] and LC25000 Dataset [22].…”

Section: Datasetsmentioning

confidence: 99%

“…To advance computer-aided automated analysis of lung and colon carcinomas, [22] released a lung and colon histopathological image dataset (LC25000 Dataset) 3 . In LC25000 Dataset, histopathological images are cropped to a square size of 768 × 768 pixels from H&E stain WSIs of lung carcinoma and colon carcinoma.…”

Section: B Lc25000 Datasetmentioning

confidence: 99%

“…We tested PDBL on three representative CNN backbones, ShuffLeNetV2, EfficientNetb0, and ResNet50 on Kather Multiclass Dataset [11] and Lung Colon Cancer Histopathological Image Dataset [22]. We conduct two main experiments, one is the effectiveness of PDBL with different proportions of the training samples, the other is the robustness of PDBL with only 1% training samples.…”

Section: Introductionmentioning

confidence: 99%

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PDBL: Improving Histopathological Tissue Classification with Plug-and-Play Pyramidal Deep-Broad Learning

Lin¹,

Han²,

Pan³

et al. 2021

Preprint

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Histopathological tissue classification is a fundamental task in pathomics cancer research. Precisely differentiating different tissue types is a benefit for the downstream researches, like cancer diagnosis, prognosis and etc. Existing works mostly leverage the popular classification backbones in computer vision to achieve histopathological tissue classification. In this paper, we proposed a super lightweight plug-and-play module, named Pyramidal Deep-Broad Learning (PDBL), for any well-trained classification backbone to further improve the classification performance without a re-training burden. We mimic how pathologists observe pathology slides in different magnifications and construct an image pyramid for the input image in order to obtain the pyramidal contextual information. For each level in the pyramid, we extract the multi-scale deep-broad features by our proposed Deep-Broad block (DB-block). We equipped PDBL in three popular classification backbones, ShuffLeNetV2, EfficientNetb0, and ResNet50 to evaluate the effectiveness and efficiency of our proposed module on two datasets (Kather Multiclass Dataset and the LC25000 Dataset). Experimental results demonstrate the proposed PDBL can steadily improve the tissue-level classification performance for any CNN backbones, especially for the lightweight models when given a small among of training samples (less than 10%), which greatly saves the computational time and annotation efforts.

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“…We evaluate our proposed PDBL in the following two datasets Kather Multiclass Dataset [11] and LC25000 Dataset [22].…”

Section: Datasetsmentioning

confidence: 99%

Section: B Lc25000 Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PDBL: Improving Histopathological Tissue Classification with Plug-and-Play Pyramidal Deep-Broad Learning

Lin¹,

Han²,

Pan³

et al. 2021

Preprint

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“…The dataset used in this research is LC25000 which is known as the lung and colon cancer histopathological image dataset [17]. The dataset was made available to fill the shortage and need of openly accessible datasets in clinical imaging [36].…”

Section: Train Test Splitmentioning

confidence: 99%

“…The images acquired from LC25000 [17] are of size 768*768. All images are cropped to square sizes of 224 x 224 pixels from the original 768 x 768 pixels.…”

Section: Data Augmentationmentioning

confidence: 99%

Prediction of lung and colon cancer through analysis of histopathological images by utilizing Pre-trained CNN models with visualization of class activation and saliency maps

Garg

2020

2020 3rd Artificial Intelligence and Cloud Computing Conference

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Colon and Lung cancer is one of the most perilous and dangerous ailments that individuals are enduring worldwide and has become a general medical problem. To lessen the risk of death, a legitimate and early finding is particularly required. In any case, it is a truly troublesome task that depends on the experience of histopathologists. If a histologist is under-prepared it may even hazard the life of a patient. As of late, deep learning has picked up energy, and it is being valued in the analysis of Medical Imaging. This paper intends to utilize and alter the current pre-trained CNN-based model to identify lung and colon cancer utilizing histopathological images with better augmentation techniques. In this paper, eight distinctive Pre-trained CNN models, VGG16, NASNetMobile, InceptionV3, InceptionResNetV2, ResNet50, Xception, MobileNet, and DenseNet169 are trained on LC25000 dataset. The model performances are assessed on precision, recall, f1score, accuracy, and auroc score. The results exhibit that all eight models accomplished noteworthy results ranging from 96% to 100% accuracy. Subsequently, GradCAM and SmoothGrad are also used to picture the attention images of Pre-trained CNN models classifying malignant and benign images. CCS Concepts: • Computing methodologies → Supervised learning by classification; Neural networks; Computer vision; • Applied computing → Health care information systems.

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DHS‐CapsNet: Dual horizontal squash capsule networks for lung and colon cancer classification from whole slide histopathological images

Adu

Cai

et al. 2021

Int J Imaging Syst Tech

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This paper proposes a new dual horizontal squash capsule network (DHS-CapsNet) to classify the lung and colon cancers on histopathological images. DHS-CapsNet is made up of encoder feature fusion (EFF) and a novel horizontal squash (HSquash) function. The EFF aggregates the extracted feature from the 2-lane convolutional layers, which provides rich information for better accuracy. HSquash is proposed as a squash function to ensure that vectors are effectively squashed and produces sparsity for a high discriminative capsule to extract important information from images with varied backgrounds. To present the effectiveness of DHS-CapsNet empirically, we applied this method on histopathological images (LC25000 dataset). We achieved better results of 99.23% compared to traditional CapsNet (85.55%). The DHS-CapsNet provides the top-1 classification error of 0.77% compared to 14.45% of the traditional CapsNet. Our results illustrate that our method improves CapsNet and can be adopted as a computer-aided diagnostic method to support doctors in lung and colon cancer diagnostics.

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Lung and Colon Cancer Histopathological Image Dataset (LC25000)

Cited by 43 publications

References 0 publications

PDBL: Improving Histopathological Tissue Classification with Plug-and-Play Pyramidal Deep-Broad Learning

PDBL: Improving Histopathological Tissue Classification with Plug-and-Play Pyramidal Deep-Broad Learning

Prediction of lung and colon cancer through analysis of histopathological images by utilizing Pre-trained CNN models with visualization of class activation and saliency maps

DHS‐CapsNet: Dual horizontal squash capsule networks for lung and colon cancer classification from whole slide histopathological images

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