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Composed image retrieval extends content-based image retrieval systems by enabling users to search using reference images and captions that describe their intention. Despite great progress in developing image-text compositors to extract discriminative visual-linguistic features, we identify a hitherto overlooked issue, triplet ambiguity, which impedes robust feature extraction. Triplet ambiguity refers to a type of semantic ambiguity that arises between the reference image, the relative caption, and the target image. It is mainly due to the limited representation of the annotated text, resulting in many noisy triplets where multiple visually dissimilar candidate images can be matched to an identical reference pair (i.e., a reference image + a relative caption). To address this challenge, we propose the Consensus Network (Css-Net), inspired by the psychological concept that groups outperform individuals. Css-Net comprises two core components: (1) a consensus module with four diverse compositors, each generating distinct image-text embeddings, fostering complementary feature extraction and mitigating dependence on any single, potentially biased compositor; (2) a Kullback–Leibler divergence loss that encourages learning of inter-compositor interactions to promote consensual outputs. During evaluation, the decisions of the four compositors are combined through a weighting scheme, enhancing overall agreement. On benchmark datasets, particularly FashionIQ, Css-Net demonstrates marked improvements. Notably, it achieves significant recall gains, with a 2.77% increase in R@10 and 6.67% boost in R@50, underscoring its competitiveness in addressing the fundamental limitations of existing methods. • We have identified an inherent issue within the context of composed image retrieval, i.e. , triplet ambiguity. • A new Consensus Network (Css-Net) featuring a consensus module with four distinct compositors for collaborative training and joint inference. • Extensive experiments verify the effectiveness of the proposed method, which is competitive with SOTA. [ABSTRACT FROM AUTHOR]

Segmentation and classification of large numbers of instances, such as cell nuclei, are crucial tasks in digital pathology for accurate diagnosis. However, the availability of high-quality datasets for deep learning methods is often limited due to the complexity of the annotation process. In this work, we investigate the impact of noisy annotations on the training and performance of a state-of-the-art CNN model for the combined task of detecting, segmenting and classifying nuclei in histopathology images. In this context, we investigate the conditions for determining an appropriate number of training epochs to prevent overfitting to annotation noise during training. Our results indicate that the utilisation of a small, correctly annotated validation set is instrumental in avoiding overfitting and maintaining model performance to a large extent. Additionally, our findings underscore the beneficial role of pre-training., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: An accurate pathological diagnosis of hepatocellular carcinoma (HCC), one of the malignant tumors with the highest mortality rate, is time-consuming and heavily reliant on the experience of a pathologist. In this report, we proposed a deep learning model that required minimal noise reduction or manual annotation by an experienced pathologist for HCC diagnosis and classification. Methods: We collected a whole-slide image of hematoxylin and eosin-stained pathological slides from 592 HCC patients at the First Affiliated Hospital, College of Medicine, Zhejiang University between 2015 and 2020. We propose a noise-specific deep learning model. The model was trained initially with 137 cases cropped into multiple-scaled datasets. Patch screening and dynamic label smoothing strategies are adopted to handle the histopathological liver image with noise annotation from the perspective of input and output. The model was then tested in an independent cohort of 455 cases with comparable tumor types and differentiations. Results: Exhaustive experiments demonstrated that our two-step method achieved 87.81% pixel-level accuracy and 98.77% slide-level accuracy in the test dataset. Furthermore, the generalization performance of our model was also verified using The Cancer Genome Atlas dataset, which contains 157 HCC pathological slides, and achieved an accuracy of 87.90%. Conclusions: The noise-specific histopathological classification model of HCC based on deep learning is effective for the dataset with noisy annotation, and it significantly improved the pixel-level accuracy of the regular convolutional neural network (CNN) model. Moreover, the model also has an advantage in detecting well-differentiated HCC and microvascular invasion. [ABSTRACT FROM AUTHOR]

BackgroundAn accurate pathological diagnosis of hepatocellular carcinoma (HCC), one of the malignant tumors with the highest mortality rate, is time-consuming and heavily reliant on the experience of a pathologist. In this report, we proposed a deep learning model that required minimal noise reduction or manual annotation by an experienced pathologist for HCC diagnosis and classification.MethodsWe collected a whole-slide image of hematoxylin and eosin-stained pathological slides from 592 HCC patients at the First Affiliated Hospital, College of Medicine, Zhejiang University between 2015 and 2020. We propose a noise-specific deep learning model. The model was trained initially with 137 cases cropped into multiple-scaled datasets. Patch screening and dynamic label smoothing strategies are adopted to handle the histopathological liver image with noise annotation from the perspective of input and output. The model was then tested in an independent cohort of 455 cases with comparable tumor types and differentiations.ResultsExhaustive experiments demonstrated that our two-step method achieved 87.81% pixel-level accuracy and 98.77% slide-level accuracy in the test dataset. Furthermore, the generalization performance of our model was also verified using The Cancer Genome Atlas dataset, which contains 157 HCC pathological slides, and achieved an accuracy of 87.90%.ConclusionsThe noise-specific histopathological classification model of HCC based on deep learning is effective for the dataset with noisy annotation, and it significantly improved the pixel-level accuracy of the regular convolutional neural network (CNN) model. Moreover, the model also has an advantage in detecting well-differentiated HCC and microvascular invasion.

Label noise can have severe negative effects on the performance of a classifier. Such noise can either arise by adversarial manipulation of the training data or from unskilled annotators frequently encountered in crowd sourcing (e.g. Amazon mechanical turk). Based on the assumption that an expert has provided some fraction of the training data, where labels can be assumed to be true, we propose a new pre-processing method to identify and correct noisy labels via a mutual consistency check using a Parzen window classifier. While the resulting optimization problem turns out to be a combinatorial problem, we design an efficient algorithm for which we provide approximation guarantees. Extensive experimental evaluation shows that our method performs similar and often much better than existing methods for the detection of noisy labels, thus leading to a boost in performance of the resulting classifiers. [ABSTRACT FROM AUTHOR]

• A detection method of ultrasound annotation noise is proposed. • A loss function is proposed to suppress the influence of noise on training. • Combined with weakly supervised learning to achieve better use of noisy annotations. Background and Objective: A large-scale training data and accurate annotations are fundamental for current segmentation networks. However, the characteristic artifacts of ultrasound images always make the annotation task complicated, such as attenuation, speckle, shadows and signal dropout. Further complications arise as the contrast between the region of interest and background is often low. Without double-check from professionals, it is hard to guarantee that there is no noisy annotation in segmentation datasets. However, among the deep learning methods applied to ultrasound segmentation so far, no one can solve this problem.Method: Given a dataset with poorly labeled masks, including a certain amount of noises, we propose an end-to-end noisy annotation tolerance network (NAT-Net). NAT-Net can detect noise by the proposed noise index (NI) and dynamically correct noisy annotations in the training stage. Simultaneously, noise index is used to correct the noise along with the output of the learning model. This method does not need any auxiliary clean datasets or prior knowledge of noise distributions, so it is more general, robust and easier to apply than the existing methods. Results: NAT-Net outperforms previous state-of-the-art methods on synthesized data with different noise ratio. For real-world dataset with more complex noise types, the IoU of NAT-Net is higher than that of state-of-art approaches by nearly 6%. Experimental results show that our method can also achieve good results compared with the existing methods for clean dataset. Conclusion: The NAT-Net reduces manual interaction of data annotation, reduces dependence on medical personnel. After tumor segmentation, disease diagnosis efficiency is improved, which provides an auxiliary strategies for subsequent medical diagnosis systems based on ultrasound. [ABSTRACT FROM AUTHOR]