A Dataset for Breast Cancer Histopathological Image Classification

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Breast cancer histopathological image classification using Convolutional Neural Networks

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Deep features for breast cancer histopathological image classification

International audienc

Multiple Instance Learning for Histopathological Breast Cancer Images

International audienceHistopathological images are the gold standard for breast cancer diagnosis. During examination several dozens of them are acquired for a single patient. Conventional, image-based classification systems make the assumption that all the patient’s images have the same label as the patient, which is rarely verified in practice since labeling the data is expensive. We propose a weakly supervised learning framework and investigate the relevance of Multiple Instance Learning (MIL) for computer-aided diagnosis of breast cancer patients, based on the analysis of histopathological images. Multiple instance learning consists in organizing instances (images) into bags (patients), without the need to label all the instances. We compare several state-of-the-art MIL methods including the pioneering ones (APR, Diverse Density, MI-SVM, citation-kNN), and more recent ones such as a non parametric method and a deep learning based approach (MIL-CNN). The experiments are conducted on the public BreaKHis dataset which contains about 8000 microscopic biopsy images of benign and malignant breast tumors, originating from 82 patients. Among the MIL methods the non-parametric approach has the best overall results, and in some cases allows to obtain classification rates never reached by conventional (single instance) classification frameworks. The comparison between MIL and single instance classification reveals the relevance of the MIL paradigm for the task at hand. In particular, the MIL allows to obtain comparable or better results than conventional (single instance) classification without the need to label all the images

Multiple Instance Learning for Histopathological Breast Cancer Images

International audienceHistopathological images are the gold standard for breast cancer diagnosis. During examination several dozens of them are acquired for a single patient. Conventional, image-based classification systems make the assumption that all the patient’s images have the same label as the patient, which is rarely verified in practice since labeling the data is expensive. We propose a weakly supervised learning framework and investigate the relevance of Multiple Instance Learning (MIL) for computer-aided diagnosis of breast cancer patients, based on the analysis of histopathological images. Multiple instance learning consists in organizing instances (images) into bags (patients), without the need to label all the instances. We compare several state-of-the-art MIL methods including the pioneering ones (APR, Diverse Density, MI-SVM, citation-kNN), and more recent ones such as a non parametric method and a deep learning based approach (MIL-CNN). The experiments are conducted on the public BreaKHis dataset which contains about 8000 microscopic biopsy images of benign and malignant breast tumors, originating from 82 patients. Among the MIL methods the non-parametric approach has the best overall results, and in some cases allows to obtain classification rates never reached by conventional (single instance) classification frameworks. The comparison between MIL and single instance classification reveals the relevance of the MIL paradigm for the task at hand. In particular, the MIL allows to obtain comparable or better results than conventional (single instance) classification without the need to label all the images

Multiple Instance Learning for Histopathological Breast Cancer Images

International audienceHistopathological images are the gold standard for breast cancer diagnosis. During examination several dozens of them are acquired for a single patient. Conventional, image-based classification systems make the assumption that all the patient’s images have the same label as the patient, which is rarely verified in practice since labeling the data is expensive. We propose a weakly supervised learning framework and investigate the relevance of Multiple Instance Learning (MIL) for computer-aided diagnosis of breast cancer patients, based on the analysis of histopathological images. Multiple instance learning consists in organizing instances (images) into bags (patients), without the need to label all the instances. We compare several state-of-the-art MIL methods including the pioneering ones (APR, Diverse Density, MI-SVM, citation-kNN), and more recent ones such as a non parametric method and a deep learning based approach (MIL-CNN). The experiments are conducted on the public BreaKHis dataset which contains about 8000 microscopic biopsy images of benign and malignant breast tumors, originating from 82 patients. Among the MIL methods the non-parametric approach has the best overall results, and in some cases allows to obtain classification rates never reached by conventional (single instance) classification frameworks. The comparison between MIL and single instance classification reveals the relevance of the MIL paradigm for the task at hand. In particular, the MIL allows to obtain comparable or better results than conventional (single instance) classification without the need to label all the images

Low-Cost Water Quality Sensors for IoT: A Systematic Review

In many countries, water quality monitoring is limited due to the high cost of logistics and professional equipment such as multiparametric probes. However, low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks, providing valuable water quality information to the public. To facilitate the widespread adoption of these sensors, it is crucial to identify which sensors can accurately measure key water quality parameters, their manufacturers, and their reliability in different environments. Although there is an increasing body of work utilizing low-cost water quality sensors, many questions remain unanswered. To address this issue, a systematic literature review was conducted to determine which low-cost sensors are being used for remote water quality monitoring. The results show that there are three primary vendors for the sensors used in the selected papers. Most sensors range in price from US$6.9 to US$169.00 but can cost up to US$500.00. While many papers suggest that low-cost sensors are suitable for water quality monitoring, few compare low-cost sensors to reference devices. Therefore, further research is necessary to determine the reliability and accuracy of low-cost sensors compared to professional devices