Enhancing Modality-Agnostic Representations via Meta-learning for Brain Tumor Segmentation.

In this work, we propose a fully automatic brain tumor and edema segmentation technique in brain magnetic resonance (MR) images. Different brain tissues are characterized using the novel texture features such as piece-wise triangular prism surface area (PTPSA), multi-fractional Brownian motion (mBm) and Gabor-like textons, along with regular intensity and intensity difference features. Classical Random Forest (RF) classifier is used to formulate the segmentation task as classification of these features in multi-modal MRIs. The segmentation performance is compared with other state-of-art works using a publicly available dataset known as Brain Tumor Segmentation (BRATS) 2012 [1]. Quantitative evaluation is done using the online evaluation tool from Kitware/MIDAS website [2]. The results show that our segmentation performance is more consistent and, on the average, outperforms other state-of-the art works in both training and challenge cases in the BRATS competition.

A literature survey of MR-based brain tumor segmentation with missing modalities.

One can find in the literature numerous techniques to reduce noise in Magnetic Resonance Images (MRI). This paper critically reviews modern de-noising algorithms (Gaussian filter, anisotropic diffusion, wavelet, and non-local mean) in terms of their efficiency, statistical assumptions, and their ability to improve brain tumor segmentation results. We will show that although different techniques do reduce the noise, many generate artifacts that are incompatible with precise brain tumor segmentation. We also show that the non-local means algorithm is the best de-noising technique for brain tumor segmentation.

Accurate segmentation of brain tumor from MRI is crucial in computer aided diagnosis as well as in other medical imaging applications. Brain tumor segmentation is a challenging task due to the diverse appearance of tumor tissues. A variety of brain tumor segmentation techniques have been explored in the literature. Here, a brief review of different brain tumor segmentation techniques has been discussed with their merits and demerits. We conclude with a discussion on the trend of future research in brain tumor segmentation.

Brain tumor segmentation and classification is really a difficult process to identify and detect the tumor region. Magnetic resonance image (MRI) gives valuable information to find the affected area in the brain. The MRI brain image is initially considered, which specifies four various modalities of the brain such as T1, T2, T1C, and the Flair. The preprocessing methodologies and the state-of-the-art MRI-related brain tumor segmentation and classification methods are discussed. This study describes the different types of brain tumor segmentation and classification techniques with its most important contributions. The survey of brain tumor segmentation and classification (BTSC) technique including the four main phases—preprocessing, feature extraction, segmentation, and classification—is discussed. The different types of BTSC techniques are listed, along with their great contributions. A review of recent articles on classifiers shows the eccentric features of classifiers for research.

In this work, we address the issues of missing modalities that have arisen from the Visual Question Answer-Difference prediction task and find a novel method to solve the task at hand. We address the missing modality-the ground truth answers-that are not present at test time and use a privileged knowledge distillation scheme to deal with the issue of the missing modality. In order to efficiently do so, we first introduce a model, the "Big" Teacher, that takes the image/question/answer triplet as its input and outperforms the baseline, then use a combination of models to distill knowledge to a target network (student) that only takes the image/question pair as its inputs. We experiment our models on the VizWiz and VQA-V2 Answer Difference datasets and show through extensive experimentation and ablation the performances of our method and a diverse possibility for future research.

This paper describes the Microsoft Text-to-Speech (TTS) system: LeanSpeech for LIMMITS (Lightweight, Multi-speaker, Multi-lingual Indic TTS) Challenge 2023 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , which is part of ICASSP2023 to encourage the advance of TTS in Indian Languages. We propose a lightweight encoder-decoder acoustic model composed of 1-D convolution and LSTM blocks, which is trained with knowledge distillation from a multi-speaker multi-lingual teacher model, DelightfulTTS [1]. The speech corpus is reprocessed and used in both AM training and vocoder fine-tuning. In Track-2 of the challenge, our system achieves MOS 4.56 and SMOS 3.98, which indicates the efficiency of the proposed model and training strategy.

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This study aims to develop a multi-modal brain tumor segmentation technique based on a multi-scale feature extraction network to enhance the accuracy of brain tumor segmentation and assist in the clinical diagnosis of neurological diseases. Built upon the advanced U-Net architecture, the study designed a network framework with residual connections, downsampling and upsampling modules, and multi-branch combination mechanisms, achieving the extraction and integration of features from multiple scales. By integrating deep learning techniques such as depthwise separable convolutions, spatial pyramid pooling (SPP), and attention mechanisms, this paper innovatively proposes a multi-scale feature fusion strategy. The network model employs a composite loss function combining cross-entropy and Dice Loss, optimized through regularization methods and the SGD algorithm, with fine-tuning of hyperparameters achieved through grid search. Experiments used datasets including multi-modal medical images such as MRI, CT, PET, and underwent rigorous data preprocessing, such as image registration and normalization, to ensure the quality of input data. The multi-modal brain tumor segmentation experiments were evaluated comprehensively using metrics such as precision, recall rate, F1 score, and ROC curves through K-fold cross-validation. Experimental results show that the network model proposed in this article reaches an advanced level on all performance metrics, exhibiting a significant advantage, especially in processing multi-modal image data, and verifying its generalization ability. Statistical significance testing further confirmed the robustness of the model, providing a powerful tool for future efficient clinical diagnosis and treatment. In conclusion, the study summarizes the innovative points and comparative advantages and looks forward to the pursuit of real-time segmentation performance and optimization of data fusion strategies.

Brain tumor segmentation from MRI data is an important but challenging task. This paper presents an efficient and fully automatic brain tumor segmentation technique. The proposed technique includes a fuzzy C-means (FCM) based preprocessing to enhance the quality of T1-weighted coronal MR images, a fast bounding box (FBB) detection algorithm to locate a rectangle around tumor, and a new dynamic snake using modified Hausdorff distance (MHD) for the final tumor extraction.

Open World Object Detection (OWOD) is a novel computer vision task with a considerable challenge, bridging the gap between classic object detection (OD) and real-world object detection. In addition to detecting and classifying seen/known objects, OWOD algorithms are expected to localize all potential unseen/unknown objects and incrementally learn them. The large pre-trained vision-language grounding models (VLM, e.g., GLIP) have rich knowledge about the open world, but are limited by text prompts and cannot localize indescribable objects. However, there are many detection scenarios in which pre-defined language descriptions are unavailable during inference. In this paper, we attempt to specialize the VLM model for OWOD tasks by distilling its open-world knowledge into a language-agnostic detector. Surprisingly, we observe that the simple knowledge distillation approach leads to unexpected performance for unknown object detection, even with a small amount of data. Unfortunately, knowledge distillation for unknown objects severely affects the learning of detectors with conventional structures, leading to catastrophic damage to the model's ability to learn about known objects. To alleviate these problems, we propose the down-weight training strategy for knowledge distillation from vision-language model to single visual modality one. Meanwhile, we propose the cascade decoupled decoders that decouple the learning of localization and recognition to reduce the impact of category interactions of known and unknown objects on the localization learning process. Ablation experiments demonstrate that both of them are effective in mitigating the impact of open-world knowledge distillation on the learning of known objects. Additionally, to alleviate the current lack of comprehensive benchmarks for evaluating the ability of the open-world detector to detect unknown objects in the open world, we refine the benchmark for evaluating the performance of unknown object detection by augmenting annotations for unknown objects which we name"IntensiveSet$\scriptstyle\spadesuit$♠". Comprehensive experiments performed on OWOD, MS-COCO, and our proposed benchmarks demonstrate the effectiveness of our methods.

The purpose of the research is to find out how deep learning and the convolutional neural network will contribute to diagnosis, early detection and segmentation of brain tumors such as glioma, benign, malignant, etc. The aim is to achieve a higher degree of segmentation quality to resolve issues related to lack of the classification accuracy and poor performance in the segmentation and detection of tumors. The presented solution is an Advanced Cascaded Anisotropic Convolutional Neural Network (CA-CNN) architecture with an optimized feature selection method. The DFP (Data collection, feature extraction & selection and prediction) taxonomy is presented that involves data acquiring, data pre-processing, feature extraction, selection and prediction methods for effective tumor segmentation and detection. The presented system will enhance the prediction accuracy and involves the genetic algorithm for effective selection of features which prevents data redundancy and reduce the delay in the detection of tumors. The utilization of genetic algorithm minimizes the redundancy within input voxels and facilitates in the optimal selection of features which improves the classification accuracy of the solution. The research conducted is to improve the brain tumor segmentation and detection process in terms of accuracy, specificity and sensitivity using multi-scale prediction and cross-validation.

The precise diagnosis and treatment planning of brain tumors significantly rely on the accurate segmentation of sub-regions from Magnetic Resonance Imaging (MRI) data. In this research, we propose a framework, D-UNET (Dilated-UNET), which enhances the traditional UNET architecture by incorporating dilated convolutions. UNET is the deep CNN architecture widely adopted for biomedical image segmentation tasks. D-UNET is specifically designed for brain tumor sub-region segmentation from multi-modal (T1, T2, T1ce, Flair) MRI images in nifti file format, each comprising 155 slices. The framework comprises of four distinct steps viz. data collection, data preprocessing, model training, and outcome evaluation. D-UNET employs two key modules during training, the dilated encoding module and the dilated decoding module. These modules enable the model to efficiently capture multi-scale contextual information, facilitating better representation learning for complex and varied tumor sub[1]regions. We evaluated the performance of D-UNET using Intersection over Union and Dice Coefficient metrics. The experimental results demonstrate that D-UNET outperforms the traditional UNET and other benchmark models in terms of segmentation accuracy. Notably, D-UNET excels in capturing finer details and intricate shapes of tumor sub-regions, contributing to its superiority in brain tumor segmentation. The ability to precisely delineate tumor sub-regions from different modalities provides crucial insights for medical professionals in treatment planning and decision-making.

In practical applications of human pose estimation, low-resolution inputs frequently occur, and existing state-of-the-art models perform poorly with low-resolution images. This work focuses on boosting the performance of low-resolution models by distilling knowledge from a high-resolution model. However, we face the challenge of feature size mismatch and class number mismatch when applying knowledge distillation to networks with different input resolutions. To address this issue, we propose a novel cross-domain knowledge distillation (CDKD) framework. In this framework, we construct a scale-adaptive projector ensemble (SAPE) module to spatially align feature maps between models of varying input resolutions. It adopts a projector ensemble to map low-resolution features into multiple common spaces and adaptively merges them based on multi-scale information to match high-resolution features. Additionally, we construct a cross-class alignment (CCA) module to solve the problem of the mismatch of class numbers. By combining an easy-to-hard training (ETHT) strategy, the CCA module further enhances the distillation performance. The effectiveness and efficiency of our approach are demonstrated by extensive experiments on three common benchmark datasets: MPII, COCO, and Crowdpose.

To examine interobserver reliability of the Acute Physiologic and Chronic Health Evaluation (APACHE) II score and identify major causes of variability in data collection. Descriptive, comparative analysis. Nine intensive care units in two teaching and six community hospitals A random sample of 342 patient records selected from a network database. None. Data were reabstracted and compared with the original records. Individual physiologic points derived from the APACHE II scoring system (instead of the actual physiologic values) were compared using the kappa statistic. Paired measurements of the continuous variables were compared using the interclass correlation coefficient and Bland-Altman plots. Excellent agreement was found in most demographic, admission, and discharge data. The system failure requiring intensive care unit admission was consistently identified by both data collectors in 88% of cases, but only 66% agreed on the exact admitting diagnosis. For APACHE II score components, the kappa statistic ranged from 0.315 for the Glasgow Coma Scale point to 0.976 for the age point. Significant disagreement regarding the probability of death derived from the APACHE II model was evident in some patient records. Overall agreement among groups of patients regarding the APACHE II score was good, however, with no significant difference in the mean score (20.2 vs. 20.1; p = .758). The predicted mortality from the reabstracted data was 30%, similar to the 27% predicted mortality from the original data (p = .380). Reliability of data collection varied widely in different components of the APACHE II probability-of-death model. Significant discrepancies in some components suggested a lack of explicit definitions and timing for consistent data collection between institutions or between data collectors. Nonetheless, variability resulting from data collection appears to be randomly distributed, so that comparisons of group means are valid.