Early-stage breast cancer is often asymptomatic, highlighting the critical role of computer-aided diagnostic(CAD) systems in mammography screening. While radiologists often refer to bilateral symmetry to identify abnormalities, most existing CAD methods analyze unilateral views or require image registration, which limits their ability to model structural heterogeneity and often introduces distortion. To address this, we propose a registration-free, structure-aware diagnostic framework that integrates bilateral mammography with soft spatial prompting via Vision Transformers (ViT). By directly concatenating bilateral images and introducing a soft attention mask generated from a lightweight segmentation network, our approach enables end-to-end modeling of cross-breast structural differences without the need for region-of-interest extraction. Extensive evaluations on both public and clinical datasets demonstrate that our method consistently outperforms CNN and lightweight Transformer baselines, achieving up to 0.930 accuracy and 0.972 AUC. To our knowledge, this is the first framework to combine bilateral structural modeling and soft guidance in a unified, interpretable, and scalable ViT-based pipeline for breast cancer diagnosis.

Accurate brain tumor segmentation is essential for clinical tasks such as diagnosis, tumor localization, and surgical planning. Although multi-modality MRI provides complementary information about tumor subregions, the difference of image characteristics across modalities poses significant challenges for effective integration. To address this, we proposed a framework for multi-modal brain tumor segmentation that enhances boundary consistency through modality-guided fusion and strengthens tumor discrimination via semantic attention enhancement. Our method incorporates two key modules: an Edge-Enhanced MultiModal Fusion (EMF) module and a Residual Convolutional Block Attention Module (ResCBAM). The EMF module, designed as an early fusion component, leverages 3D Sobel and Laplacian filters to extract structural features and selectively integrates T1c with other modalities to improve boundary-aware representation. At the network bottleneck, ResCBAM combines residual connections with channel and spatial attention to enhance high-level semantic features. Extensive experiments on the BraTS2023 public dataset demonstrate strong segmentation performance, and further evaluation on the external BraTS2025 dataset confirms the robustness of our approach. Ablation studies demonstrate that EMF plays a key role in extracting high-quality, modality-aware representations that can be effectively refined by ResCBAM, highlighting the effectiveness of this cooperative fusion design.

The decline in traditional autopsy practices has led to the rise of autopsy imaging as a non-invasive alternative. However, the shortage of forensic pathologists skilled in postmortem image interpretation presents a significant challenge. Our study addresses this gap by advancing the capabilities of computer-aided diagnosis (CAD) systems in forensic diagnosis. This study builds on a previous work which developed a CAD system for drowning diagnosis and identified a critical limitation: the inconsistency between human expertise and the decision basis of the CAD system. To alleviate this issue, we present an end-to-end CAD system based on Y-Net that not only classifies post-mortem images into drowning or non-drowning but also segments regions of interest in alignment with human expertise. Experiment results showed that the model achieved an accuracy of 0.92 for classification and a mean squared error of 0.04 for segmentation, offering a promising performance and medically consistent results in drowning diagnosis.

Deep learning has proven to be a powerful tool for analyzing complex medical data; however, its effectiveness can be hindered by limited training data, leading to overfitting. In response to this challenge, our paper proposes a novel deep learning-based method that integrates attention optimization to mitigate overfitting when training on few instances. We introduce a unique loss function that not only considers the disparity between predicted scores and class labels but also accounts for the distinction between the model’s attention and human observations. By optimizing for this loss, our model is encouraged to learn essential features identified by experts, enhancing its classification capabilities. To validate the effectiveness of our approach, we focus on the classification of post-mortem computed tomography (PMCT) as a benchmark task, showcasing improved performance even in scenarios with limited training data. Our contributions offer a promising avenue for enhancing the robustness of deep learning models in medical applications with constrained datasets.

It is challenging to diagnose drowning in autopsy even with the help of post-mortem multi-slice computed tomography (MSCT) due to the complex pathophysiology and the shortage of forensic specialists equipped with radiology knowledge. Therefore, a computer-aided diagnosis (CAD) system was developed to help with diagnosis. Most deep learning-based CAD systems only utilize 2D information, which is proper for 2D data such as chest X-ray images. However, 3D information should also be considered for 3D data like CT. Conventional 3D methods require a huge amount of data and computational cost when using 3D methods. In this article, we proposed a 2.5D method that converts 3D data into 2D images to train 2D deep learning models for drowning diagnosis. The key point of this 2.5D method is that it uses a subset to represent the whole case, covering this case as much as possible while avoiding other repetitive information. To evaluate the effectiveness of the proposed method, conventional 2D, previous 2.5D, and 3D deep learning-based methods were tested using an MSCT dataset obtained from Tohoku university. Then, to provide explainable diagnosis results, a visualization method called Gradient-weighted Class Activation Mapping was employed to visualize features relevant to drowning in CT images. Results on drowning diagnosis showed that our proposed method achieved the best performance compared to other 2D, 2.5D, and 3D methods. The visual assessment also demonstrated that our method could find the saliency regions corresponding to drowning.

Drowning death rate is high in Japan and its diagnosis is still one of the most challenging tasks in the field of forensics due to the complex interpretation of its pathology. Postmortem lungs computed tomography (CT) images can be used for interpretation of forensic pathology due to its benefits but shortage of specialists is a critical problem. Also, manually interpreting CT images is a tiring and time-taking process. In this paper, we proposed a computer-aided diagnosis system based on a deep convolutional neural network (DCNN) for classifying the post-mortem lungs CT images into drowning and non-drowning. A pre-trained DCNN was implemented in this study for classification of post-mortem lungs CT images. The DCNN was trained and tested using a post-mortem lungs CT image database obtained from Tohoku University Autopsy Imaging Center. The training process involves fine-tuning. The experimental results demonstrated a receiver operating characteristic (ROC) curve and an area under the curve (AUC) of 95 percent was achieved in drowning detection using the post-mortem lungs CT images.

The usefulness of taking mammography has widely been recognized, but screening mammography occasionally results in an excessive recommendation for subsequent biopsy causing many women inconvenience and severe anxiety. Especially, there is a high chance of unnecessary biopsy recommendation for those findings which are difficult to be classified into malignancy and benignancy. However, few have focused on the computer-aided diagnosis (CAD) performance for such difficult cases. To address this problem, we developed a deep learning based classification technique to aid the difficult diagnosis. We evaluated 100 benign and malignant masses of the breast imaging-reporting and data system (BI-RADS) Category 4 that are generally difficult to be classified into malignant and benign. Five certificated doctors participated in the experiments where each doctor reads the 100 images alone first and a week later reads again with the proposed CAD system. The area under the receiver operating characteristic curve (AUC-ROC) for the CAD system was 0.79. This is greater than 0.65, the average value of the human readers' AUC-ROCs, while the average value of the human readers' AUC-ROCs reached the best value of 0.8 when they used the CAD system. These results suggest that the proposed CAD system is able to not only outperform human readers in classifying the masses, but also enhance the human performance in this difficult task.

This paper proposes a motion detection method based on Gaussian mixture model for detecting the moving objects in old film sequences. Conventional motion detection techniques can hardly be applied to the old film sequences due to the influence of intensity flicker. The proposed method combines an adaptive mixture method with a motion detection. The intensities of a particular pixel along the temporal direction are modeled as the Gaussian mixture model. The adaptive mixture method can automatically determine the number of components in the Gaussian mixture model and estimate the parameters of each Gaussian component. Then the components with small weights are chosen as the components corresponding to the moving objects. Experimental results show that the proposed method can effectively detect the moving objects in old film sequences.