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During the past five years, there has been an increasing trend of weakly supervised crowd counting methods being developed since such methods just rely on count-level annotations and avoid a laborious labeling process. But, the existing weakly supervised methods usually fail to achieve comparable counting performance to the fully supervised methods. To improve the accuracy of crowd counting tasks, we propose to combine the convolutional neural network (CNN) and Transformer frameworks. Since CNN focuses on capturing local detail information and Transformer can effectively extract global context information, we believe that the combination of CNN and Transformer could learn more efficient feature representations for crowd images. Our proposed framework is named CrowdCCT (Crowd Counting via CNN and Transformer), and it is composed of a CNN feature extraction part, a Transformer feature extraction part, and a counting regression part. In the CNN part, we utilize DenseNet121 to learn rich semantic features with its inherent dense connection structure. In the Transformer part, we introduce two attention modules, Multi-Scale Dilated Attention (MSDA) and Location-Enhanced Attention (LEA), working together to extract more expressive features. The output features are then fed into the regression part to generate the predicted counting results. Experiments on four crowd counting benchmark datasets demonstrate that our proposed CrowdCCT can achieve superior performance. Also, the experimental results validate the feasibility and effectiveness of combining CNN and Transformer for weakly supervised counting tasks. Our work could be expected to promote further combination research on CNN and Transformer. [ABSTRACT FROM AUTHOR]

Salient object detection (SOD) aims to detect the most attractive region in an image. Fully supervised SOD based on deep learning usually needs a large amount of data with human annotation. Researchers have gradually focused on the SOD task using weakly supervised annotation such as category, scribble, and bounding-box, while these existing weakly supervised methods achieve limited performance and demonstrate a huge performance gap with fully supervised methods. In this work, we proposed one novel two-stage weakly supervised method based on bounding-box annotation and the recent large visual model Segment Anything (SAM). In the first stage, we regarded the bounding-box annotation as the box prompt of SAM to generate initial labels and proposed object completeness check and object inversion check to exclude low quality labels, then we selected reliable pseudo labels for the training initial SOD model. In the second stage, we used the initial SOD model to predict the saliency map of excluded images and adopted SAM with the everything mode to generate segmentation candidates, then we fused the saliency map and segmentation candidates to predict pseudo labels. Finally we used all reliable pseudo labels generated in the two stages to train one refined SOD model. We also designed a simple but effective SOD model, which can capture rich global context information. Performance evaluation on four public datasets showed that the proposed method significantly outperforms other weakly supervised methods and also achieves comparable performance with fully supervised methods. [ABSTRACT FROM AUTHOR]

Despite the success of deep learning in land cover classification, high-resolution (HR) land cover mapping remains challenging due to the time-consuming and labor-intensive process of collecting training samples. Many global land cover products (LCP) can reflect the low-level commonality (LLC) knowledge of land covers, such as basic shape and underlying semantic information. Therefore, we expect to use LCP as weakly supervised information to guide the semantic segmentation of HR images. We regard high-level specialty (HLS) knowledge as HR information unavailable in the LCP. We believe LLC knowledge can gradually evolve into HLS knowledge through self-active learning. Hence, we design a knowledge evolution learning strategy from LLC to HLS knowledge and correspondingly devise a knowledge evolution weakly supervised learning framework (KE-WESUP) based on LCP. KE-WESUP mainly includes three tasks: (1) Abstraction of LLC knowledge. KE-WESUP first adopts a training method based on superpixel to alleviate the inconsistency between LCP and HR images and directly learns the LLC knowledge from LCP according to the feature-fitting capacity of convolutional neural networks. (2) Automatic exploration of HLS knowledge. We propose a dynamic label optimization strategy to obtain a small number of point labels with high confidence and encourage the model to automatically mine HLS knowledge through the knowledge exploration mechanism, which prompts the model to adapt to complexHR scenes. (3) Dynamic interaction of LLC and HLS knowledge. We adopt the consistency regularization method to achieve further optimization and verification of LLC and HLS knowledge. To verify the effectiveness of KE-WESUP, we conduct experiments on USDA National Agriculture Imagery Program (1 m) and GaoFen-2 (1 m) data using WolrdCover (10 m) as labels. The results show that KE-WESUP achieves outstanding results in both experiments, which has significant advantages over existing weakly supervised methods. Therefore, the proposed method has great potential in utilizing the prior information of LCP and is expected to become a new paradigm for large-scale HR land cover classification. [ABSTRACT FROM AUTHOR]

Weakly supervised semantic segmentation (WSSS) is a challenging task of computer vision. The state-of-the-art semantic segmentation methods are usually based on the convolutional neural network (CNN), which mainly have the drawbacks of inability to explore the global information correctly and failure to activate potential object regions. To avoid such drawbacks, the transformer approach is explored in the WSSS task, but no effective semantic association between different patch tokens can be determined in the transformer. To address this issue, inspired by the graph convolutional network (GCN), this paper proposes a graph structure to learn the semantic category relationships between different blocks in the vector sequence. To verify the effectiveness of the proposed method in this paper, a large number of experiments were conducted on the publicly available PASCAL VOC2012 dataset. The experimental results show that our proposed method achieves significant performance improvement in the WSSS task and outperforms other state-of-the-art transformer-based methods. [ABSTRACT FROM AUTHOR]

Aiming to address the issue of complex preprocessing and post-processing required by existing crowd localization methods that employ pseudo bounding boxes or pre-designed localization maps, an end-to-end crowd localization network based on weakly supervised, LocalFormer, is proposed. In the feature extraction stage, a pure Transformer is used as the backbone network, and a global max-pooling operation is performed on the features of each stage to extract more comprehensive details of human heads. In the encoder-decoder stage, the positional information is embedded into the aggregated features as input to the encoder. Each decoder layer uses a set of trainable embeddings as queries, and takes visual features of the last layer of the encoder as keys and values. The decoded features are then used to predict confidence scores. Finally, a binary module is introduced with an adaptive optimized threshold learner to precisely binarize the confidence maps. Extensive experiments on three datasets in different environments show that the proposed method achieves the best positioning performance. [ABSTRACT FROM AUTHOR]

The detection of skin lesions involves a resource-intensive and time-consuming process, necessitating specialized equipment and the expertise of dermatologists within medical facilities. Lesion segmentation, as a critical aspect of skin disorder assessment, has garnered substantial attention in recent research pursuits. In response, we developed a portable automatic dermatology detector and proposed a dual-CAM weakly supervised bootstrapping model for skin lesion detection. The hardware system in our device utilizes a modular and miniaturized design, including an embedded board, dermatoscope, and display, making it highly portable and easy to use in various settings. Our software solution uses a convolutional neural network (CNN) with a dual-class activation map (CAM) weakly supervised bootstrapping model for skin lesion detection. The model boasts two key characteristics: the integration of segmentation and classification networks, and the utilization of a dual CAM structure for precise lesion localization. We conducted an evaluation of our method using the ISIC2016 and ISIC2017 datasets, which yielded findings that demonstrate an AUC of 86.3% for skin lesion classification for ISIC2016 and an average AUC of 92.9% for ISIC2017. Furthermore, our system achieved diagnostic results of significant reference value, with an average AUC of 92% when tested on real-life skin. The experimental results underscore the portable device's capacity to provide reliable diagnostic information for potential skin lesions, thereby demonstrating its practical applicability. [ABSTRACT FROM AUTHOR]

Advances in deep learning and computer vision are making significant contributions to flood mapping, particularly when integrated with remotely sensed data. Although existing supervised methods, especially deep convolutional neural networks, have proved to be effective, they require intensive manual labeling of flooded pixels to train a multi-layer deep neural network that learns abstract semantic features of the input data. This research introduces a novel weakly supervised approach for pixel-wise flood mapping by leveraging multi-temporal remote sensing imagery and image processing techniques (e.g., Normalized Difference Water Index and edge detection) to create weakly labeled data. Using these weakly labeled data, a bi-temporal U-Net model is then proposed and trained for flood detection without the need for time-consuming and labor-intensive human annotations. Using floods from Hurricanes Florence and Harvey as case studies, we evaluated the performance of the proposed bi-temporal U-Net model and baseline models, such as decision tree, random forest, gradient boost, and adaptive boosting classifiers. To assess the effectiveness of our approach, we conducted a comprehensive assessment that (1) covered multiple test sites with varying degrees of urbanization, and (2) utilized both bi-temporal (i.e., pre- and post-flood) and uni-temporal (i.e., only post-flood) input. The experimental results showed that the proposed framework of weakly labeled data generation and the bi-temporal U-Net could produce near real-time urban flood maps with consistently high precision, recall, f1 score, IoU score, and overall accuracy compared with baseline machine learning algorithms. [ABSTRACT FROM AUTHOR]

Weakly supervised semantic segmentation is receiving great attention due to its low human annotation cost. In this paper, we aim to tackle bounding box supervised semantic segmentation, i.e., training accurate semantic segmentation models using bounding box annotations as supervision. To this end, we propose affinity attention graph neural network ($A^2$ A 2 GNN). Following previous practices, we first generate pseudo semantic-aware seeds, which are then formed into semantic graphs based on our newly proposed affinity Convolutional Neural Network (CNN). Then the built graphs are input to our $A^2$ A 2 GNN, in which an affinity attention layer is designed to acquire the short- and long- distance information from soft graph edges to accurately propagate semantic labels from the confident seeds to the unlabeled pixels. However, to guarantee the precision of the seeds, we only adopt a limited number of confident pixel seed labels for $A^2$ A 2 GNN, which may lead to insufficient supervision for training. To alleviate this issue, we further introduce a new loss function and a consistency-checking mechanism to leverage the bounding box constraint, so that more reliable guidance can be included for the model optimization. Experiments show that our approach achieves new state-of-the-art performances on Pascal VOC 2012 datasets (val: 76.5 percent, test: 75.2 percent). More importantly, our approach can be readily applied to bounding box supervised instance segmentation task or other weakly supervised semantic segmentation tasks, with state-of-the-art or comparable performance among almot all weakly supervised tasks on PASCAL VOC or COCO dataset. Our source code will be available at https://github.com/zbf1991/A2GNN. [ABSTRACT FROM AUTHOR]

Background: Enteral nutrition through feeding tubes serves as the primary method of nutritional supplementation for patients unable to feed themselves. Plain radiographs are routinely used to confirm the position of the Nasoenteric feeding tubes the following insertion and before the commencement of tube feeds. Convolutional neural networks (CNNs) have shown encouraging results in assisting the tube positioning assessment. However, robust CNNs are often trained using large amounts of manually annotated data, which challenges applying CNNs on enteral feeding tube positioning assessment. Method: We build a CNN model for feeding tube positioning assessment by pre-training the model under a weakly supervised fashion on large quantities of radiographs. Since most of the model was pre-trained, a small amount of labeled data is needed when fine-tuning the model for tube positioning assessment. We demonstrate the proposed method using a small dataset with 175 radiographs. Result: The experimental result shows that the proposed model improves the area under the receiver operating characteristic curve (AUC) by up to 35.71% , from 0.56 to 0.76, and 14.49% on the accuracy, from 0.69 to 0.79 when compared with the no pre-trained method. The proposed method also has up to 40% less error when estimating its prediction confidence. Conclusion: Our evaluation results show that the proposed model has a high prediction accuracy and a more accurate estimated prediction confidence when compared to the no pre-trained model and other baseline models. The proposed method can be potentially used for assessing the enteral tube positioning. It also provides a strong baseline for future studies. [ABSTRACT FROM AUTHOR]

Obtaining extensive, high-quality datasets for crack segmentation with pixel-level labels is expensive and labor-intensive. The Unified Weakly and Semi-supervised Crack Segmentation (UWSCS) framework addresses this challenge by leveraging a limited number of images with coarse labels and a larger set of unlabeled images. Two label correction modules, based on super-pixel segmentation and a shrink module, are incorporated in the model training to improve crack label accuracy and optimize edge refinement. UWSCS employs a dual-encoder fusion network, combining transformers and convolutional neural networks, to enhance crack segmentation in complex backgrounds. An enhanced algorithm using the medial axis transform is proposed for accurately quantifying crack length and width. Extensive experiments were conducted on both synthetic and real crack datasets to validate the superior performance of UWSCS. The results underscore the significant impact of label quality and quantity used in training on model prediction accuracy. Trained on a concrete crack dataset with limited coarse labels, UWSCS achieves an Intersection of Union (IoU) of 77.53%, surpassing the fully supervised model using the same number of coarse labels by 28.64%. It closely approaches the performance of a fully supervised model with the same number of fine labels (IoU of 80.21%). UWSCS outperforms other advanced networks and semi-supervised/weakly supervised algorithms when trained with a limited set of more cost-effective manually labeled coarse labels. Integrated with the crack segmentation network, super-pixel segmentation, and shrink modules during training, UWSCS with limited coarse labels performs similarly to a fully supervised model using fine labels, thereby reducing manual labeling costs by over 90% and enhancing detection efficiency in practical engineering. [ABSTRACT FROM AUTHOR]

Weakly supervised semantic segmentation under image-level label supervision has undergone impressive improvements over the past years. These approaches can significantly reduce human annotation efforts, although they remain inferior to fully supervised procedures. In this paper, we propose a novel framework that iteratively refines pixel-level annotations and optimizes segmentation network. We first produce initial deep cues using the combination of activation maps and a saliency map. To produce high-quality pixel-level annotations, a graphical model is constructed over optimal segmentation of high-quality region hierarchies to propagate information from deep cues to unmarked regions. In the training process, the initial pixel-level annotations are used as supervision to train the segmentation network and to predict segmentation masks. To correct inaccurate labels of segmentation masks, we use these segmentation masks with the graphical model to produce accurate pixel-level annotations and use them as supervision to retrain the segmentation network. Experimental results show that the proposed method can significantly outperform the weakly-supervised semantic segmentation methods using static labels. The proposed method has state-of-the-art performance, which are 66.7 % mIoU score on PASCAL VOC 2012 test set and 27.0 % mIoU score on MS COCO validation set. [ABSTRACT FROM AUTHOR]

Prostate cancer is one of the main diseases affecting men worldwide. The gold standard for diagnosis and prognosis is the Gleason grading system. In this process, pathologists manually analyze prostate histology slides under microscope, in a high time-consuming and subjective task. In the last years, computer-aided-diagnosis (CAD) systems have emerged as a promising tool that could support pathologists in the daily clinical practice. Nevertheless, these systems are usually trained using tedious and prone-to-error pixel-level annotations of Gleason grades in the tissue. To alleviate the need of manual pixel-wise labeling, just a handful of works have been presented in the literature. Furthermore, despite the promising results achieved on global scoring the location of cancerous patterns in the tissue is only qualitatively addressed. These heatmaps of tumor regions, however, are crucial to the reliability of CAD systems as they provide explainability to the system's output and give confidence to pathologists that the model is focusing on medical relevant features. Motivated by this, we propose a novel weakly-supervised deep-learning model, based on self-learning CNNs, that leverages only the global Gleason score of gigapixel whole slide images during training to accurately perform both, grading of patch-level patterns and biopsy-level scoring. To evaluate the performance of the proposed method, we perform extensive experiments on three different external datasets for the patch-level Gleason grading, and on two different test sets for global Grade Group prediction. We empirically demonstrate that our approach outperforms its supervised counterpart on patch-level Gleason grading by a large margin, as well as state-of-the-art methods on global biopsy-level scoring. Particularly, the proposed model brings an average improvement on the Cohen's quadratic kappa (κ) score of nearly 18% compared to full-supervision for the patch-level Gleason grading task. This suggests that the absence of the annotator's bias in our approach and the capability of using large weakly labeled datasets during training leads to higher performing and more robust models. Furthermore, raw features obtained from the patch-level classifier showed to generalize better than previous approaches in the literature to the subjective global biopsy-level scoring. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) trained on the pixel-wise annotated images have dramatically improved the state-of-the-art in semantic segmentation. However, due to the high cost of labeling training data, its application has great limitation. In this paper, we propose a DCNNs model for generating the pixel-level labels using the image-level annotation. The model consists of an encoder-decoder, a feature decomposer, and a multi-label classifier. The encoder extracts the deep convolutional feature maps of the input image. The feature decomposer can decompose the convolutional feature extracted by the encoder into feature components of different semantics. The decomposer is based on the orthogonal non-negative matrix factorization (NMF) technology. The function of the decoder is to map the feature components of different semantics to the input resolution images. The decoder uses the position index of maximum pooling provided by the corresponding encoder to perform non-linear up-sampling, which eliminates the learning requirement of up-sampling. Since the image reconstruction is conducted according to the semantic categories, image regions of different semantics are restored to different images. Then, the regions of different semantics can be segmented through the posted-processing algorithms. The experimental results on open data set show that the proposed model outperforms some recently developed methods.

Box level annotation of a large number of logo images for training purpose of typical deep learning architecture is highly challenging. Thus, a method that can detect the logo with the help of training to remove box-level annotations can be helpful. In this paper, we present a method of logo detection that utilizes weakly supervised learning of Convolutional Neural Network (CNN) to generate a deep saliency map. The saliency map is generated from the back-propagated response of the CNN trained with the classification task. The saliency map produces responses for the regions of logos. GrabCut segmentation method has been applied then to obtain the bounding box corresponding to the logo class predicted by the CNN for a given image. AlexNet, CaffeNet, and VGGNet deep architectures has been fine-tuned for the classification purpose. The framework is further utilized for detection through a back-propagated saliency map. The performance of the proposed methodology has been validated on the FlickrLogos-32 logo benchmark dataset. The proposed method outperforms the state-of-the-art baseline fully supervised methods with mean average precision (mAP) of 75.83%. [ABSTRACT FROM AUTHOR]

Image semantic segmentation is one of the most important tasks in the field of computer vision, and it has made great progress in many applications. Many fully supervised deep learning models are designed to implement complex semantic segmentation tasks and the experimental results are remarkable. However, the acquisition of pixel-level labels in fully supervised learning is time consuming and laborious, semi-supervised and weakly supervised learning is gradually replacing fully supervised learning, thus achieving good results at a lower cost. Based on the commonly used models such as convolutional neural networks, fully convolutional networks, generative adversarial networks, this paper focuses on the core methods and reviews the semi- and weakly supervised semantic segmentation models in recent years. In the following chapters, existing evaluations and data sets are summarized in details and the experimental results are analyzed according to the data set. The last part of the paper is an objective summary. In addition, it points out the possible direction of research and inspiring suggestions for future work. [ABSTRACT FROM AUTHOR]

Object detection is an important research problem in computer vision. Convolutional Neural Networks (CNN) based deep learning models could be used for this problem, but it would require a large number of manual annotated objects for training or fine-tuning. Unfortunately, fine-grained manually annotated objects are not available in many cases. Usually, it is possible to obtain imperfect initialized detections by some weak object detectors using some weak supervisions like the prior knowledge of shape, size or motion. In some real-world applications, objects have little inter-occlusions and split/merge difficulties, so the spatio-temporal consistency in object tracking are well preserved in the image sequences/videos. Starting from the imperfect initialization, this paper proposes a new easy-to-hard learning method to incrementally improve the object detection in image sequences/videos by an unsupervised spatio-temporal analysis which involves more complex examples that are hard for object detection for next-iteration training. The proposed method does not require manual annotations, but uses weak supervisions and spatio-temporal consistency in tracking to simulate the supervisions in the CNN training. Experimental results on three different tasks show significant improvements over the initialized detections by the weak object detectors. [ABSTRACT FROM AUTHOR]

Feedback is a fundamental mechanism existing in the human visual system, but has not been explored deeply in designing computer vision algorithms. In this paper, we claim that feedback plays a critical role in understanding convolutional neural networks (CNNs), e.g., how a neuron in CNNs describes an object's pattern, and how a collection of neurons form comprehensive perception to an object. To model the feedback in CNNs, we propose a novel model named Feedback CNN and develop two new processing algorithms, i.e., neural pathway pruning and pattern recovering. We mathematically prove that the proposed method can reach local optimum. Note that Feedback CNN belongs to weakly supervised methods and can be trained only using category-level labels. But it possesses a powerful capability to accurately localize and segment category-specific objects. We conduct extensive visualization analysis, and the results reveal the close relationship between neurons and object parts in Feedback CNN. Finally, we evaluate the proposed Feedback CNN over the tasks of weakly supervised object localization and segmentation, and the experimental results on ImageNet and Pascal VOC show that our method remarkably outperforms the state-of-the-art ones. [ABSTRACT FROM AUTHOR]

Weakly supervised salient object detection (SOD) is a challenging task and has drawn much attention from several research perspectives, it has revealed two problems while driving the rapid development of saliency detection. (1) Large divergence in the characteristics of saliency regions in terms of location, shape and size makes them difficult to recognize. (2) The properties of convolutional neural networks dictate that it is insensitive to various transformations, which will lead to hardly balance the application of various disturbances. To tackle these limitations, this paper proposes a novel seminar learning framework with consistent transformation ensembling (SLF-CT) for scribble supervised SOD. The framework consists of the teacher–student model and the student–student model for segmenting the salient objects. Specifically, we first design a cross attention guided network (CAGNet) as a baseline model for saliency prediction. Then we assign CAGNet to the teacher–student model, where the teacher network is based on the exponential moving average and guides the training of the student network. Moreover, we adopt multiple pseudo labels to transfer the information among students from different conditions. To further enhance the regularization of the network, a consistency transformation mechanism is also incorporated, which encourages the saliency prediction and input image of the network to be consistent. The experimental results demonstrate that the proposed approach performs favorably comparable with the state-of-the-art weakly supervised methods. As far as we know, the proposed approach is the first application of seminar learning in the SOD area. [ABSTRACT FROM AUTHOR]

Recently, convolutional neural networks (CNNs) directly using whole slide images (WSIs) for tumor diagnosis and analysis have attracted considerable attention, because they only utilize the slide-level label for model training without any additional annotations. However, it is still a challenging task to directly handle gigapixel WSIs, due to the billions of pixels and intra-variations in each WSI. To overcome this problem, in this paper, we propose a novel end-to-end interpretable deep MIL framework for WSI analysis, by using a two-branch deep neural network and a multi-scale representation attention mechanism to directly extract features from all patches of each WSI. Specifically, we first divide each WSI into bag-, patch- and cell-level images, and then assign the slide-level label to its corresponding bag-level images, so that WSI classification becomes a MIL problem. Additionally, we design a novel multi-scale representation attention mechanism, and embed it into a two-branch deep network to simultaneously mine the bag with a correct label, the significant patches and their cell-level information. Extensive experiments demonstrate the superior performance of the proposed framework over recent state-of-the-art methods, in term of classification accuracy and model interpretability. All source codes are released at : https://github.com/xhangchen/MRAN/. • We propose a novel end-to-end interpretable deep MIL framework to extract all patch-level features of each WSI. • Our framework is to directly solve the weakly supervised gigapixel WSI classification problem. • We propose a multi-scale representation attention mechanism to simultaneously mine the significant bag-, patch- and cell-level images for WSI analysis. [ABSTRACT FROM AUTHOR]

• Segmentation of Gleason grades in prostate histology images using global Gleason score during training. • A weakly-supervised architecture that perform multi-class segmentation, including the background class. • Log-sum exponential pooling allows to adapt the weakly-supervised architecture to the extent of cancerous structures in the tissue. • Weakly-supervised CNNs perform at the same level as fully-supervised techniques for Gleason grading of tissue micro-arrays. Prostate cancer is one of the main diseases affecting men worldwide. The Gleason scoring system is the primary diagnostic tool for prostate cancer. This is obtained via the visual analysis of cancerous patterns in prostate biopsies performed by expert pathologists, and the aggregation of the main Gleason grades in a combined score. Computer-aided diagnosis systems allow to reduce the workload of pathologists and increase the objectivity. Nevertheless, those require a large number of labeled samples, with pixel-level annotations performed by expert pathologists, to be developed. Recently, efforts have been made in the literature to develop algorithms aiming the direct estimation of the global Gleason score at biopsy/core level with global labels. However, these algorithms do not cover the accurate localization of the Gleason patterns into the tissue. These location maps are the basis to provide a reliable computer-aided diagnosis system to the experts to be used in clinical practice by pathologists. In this work, we propose a deep-learning-based system able to detect local cancerous patterns in the prostate tissue using only the global-level Gleason score obtained from clinical records during training. The methodological core of this work is the proposed weakly-supervised-trained convolutional neural network, WeGleNet, based on a multi-class segmentation layer after the feature extraction module, a global-aggregation, and the slicing of the background class for the model loss estimation during training. Using a public dataset of prostate tissue-micro arrays, we obtained a Cohen's quadratic kappa (κ) of 0.67 for the pixel-level prediction of cancerous patterns in the validation cohort. We compared the model performance for semantic segmentation of Gleason grades with supervised state-of-the-art architectures in the test cohort. We obtained a pixel-level κ of 0.61 and a macro-averaged f1-score of 0.58 , at the same level as fully-supervised methods. Regarding the estimation of the core-level Gleason score, we obtained a κ of 0.76 and 0.67 between the model and two different pathologists. WeGleNet is capable of performing the semantic segmentation of Gleason grades similarly to fully-supervised methods without requiring pixel-level annotations. Moreover, the model reached a performance at the same level as inter-pathologist agreement for the global Gleason scoring of the cores. [ABSTRACT FROM AUTHOR]