Deep Machine Learning Histopathological Image Analysis for Renal Cancer Detection

Koo, Jia Chun; Hum, Yan Chai; Lai, Khin Wee; Yap, Wun‐She; Manickam, Swaminathan; Tee, Yee Kai

doi:10.1145/3532213.3532313

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Later, the deep learning approach is introduced to automate feature selection. Many works have reported deep convolutional neural networks (CNN) achieve promising performance in histopathological image classification and segmentation tasks in cancer [17][18][19], metastasis [20,21], and gene mutation [22,23] analysis; some even reported performance comparable to pathologists' assessment [9,[24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning

et al. 2023

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Gastric cancer is a leading cause of cancer-related deaths worldwide, underscoring the need for early detection to improve patient survival rates. The current clinical gold standard for detection is histopathological image analysis, but this process is manual, laborious, and time-consuming. As a result, there has been growing interest in developing computer-aided diagnosis to assist pathologists. Deep learning has shown promise in this regard, but each model can only extract a limited number of image features for classification. To overcome this limitation and improve classification performance, this study proposes ensemble models that combine the decisions of several deep learning models. To evaluate the effectiveness of the proposed models, we tested their performance on the publicly available gastric cancer dataset, Gastric Histopathology Sub-size Image Database. Our experimental results showed that the top 5 ensemble model achieved state-of-the-art detection accuracy in all sub-databases, with the highest detection accuracy of 99.20% in the 160 × 160 pixels sub-database. These results demonstrated that ensemble models could extract important features from smaller patch sizes and achieve promising performance. Overall, our proposed work could assist pathologists in detecting gastric cancer through histopathological image analysis and contribute to early gastric cancer detection to improve patient survival rates.

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Section: Introductionmentioning

confidence: 99%

Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning

et al. 2023

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“…This is reinforced by the exceptional performance of deep learning models in histopathology image classification [1], [2], [3], [4], [5], [6], [7] and segmentation [8], [9], [10], [11] tasks. This high performance extends across a range of subjects of interest, including cancer [12], [13], [14], [15], [16], metastases [17], [18], [19], and gene mutation [20], [21], [22]. The exceptional performance and efficiency showcased by computational pathology using deep learning can effectively address the limitations of conventional HIA and provide valuable assistance to pathologists in their diagnostic tasks, thereby alleviating the burden on pathologists.…”

Section: Introductionmentioning

confidence: 93%

Histopathological Cancer Detection Using Intra-Domain Transfer Learning and Ensemble Learning

Yong,

Hum,

Lai

et al. 2024

IEEE Access

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Cancer is a leading global cause of death. Histopathology image analysis is widely recognized as the gold standard for cancer diagnosis, playing a crucial role in early detection and reducing mortality rates. However, this diagnostic task is performed manually by pathologists, leading to high human errors and variabilities due to the huge number of images to screen and tissue complexities. With the emergence of deep learning, specifically the Convolutional Neural Network (CNN), in computer-aided diagnosis, there is a growing interest from the medical community to automate the labor-intensive manual image screening. Despite its promising performance, deep learning models still encounter challenges when it comes to extracting comprehensive histopathological features for optimal results. To tackle this issue, our study introduces deep learning models based on intra-domain transfer learning and ensemble learning. We evaluated these models on public histopathology datasets, including Gastric Histopathology Sub-size Image Database (GasHisSDB), Chaoyang colorectal, and Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Cell Carcinoma (CPTAC-CCRCC). Our models achieved state-of-the-art accuracy: 99.78% on GasHisSDB, 85.69% on Chaoyang, and 99.17% on CPTAC-CCRCC. These results highlight our models' ability to extract rich features and perform well on low-resolution histopathology images. Thus, our models have the potential to assist pathologists, reduce their workload, and enhance patient survival rates.

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Histopathological Gastric Cancer Detection Using Transfer Learning

Yong,

Hum,

Lai

et al. 2023

2023 11th International Conference on Bioinformatics and Computational Biology (ICBCB)

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Deep Machine Learning Histopathological Image Analysis for Renal Cancer Detection

Cited by 3 publications

References 12 publications

Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning

Histopathological Gastric Cancer Detection on GasHisSDB Dataset Using Deep Ensemble Learning

Histopathological Cancer Detection Using Intra-Domain Transfer Learning and Ensemble Learning

Histopathological Gastric Cancer Detection Using Transfer Learning

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