Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainability

It is imperative that breast cancer is detected precisely and timely to improve patient outcomes. Diagnostic methodologies have traditionally relied on unimodal approaches; however, medical data analytics is integrating diverse data sources beyond conventional imaging. Using multi-modal techniques, integrating both image and non-image data, marks a transformative advancement in breast cancer diagnosis. The purpose of this review is to explore the burgeoning field of multimodal techniques, particularly the fusion of histopathology images with non-image data. Further, Explainable AI (XAI) will be used to elucidate the decision-making processes of complex algorithms, emphasizing the necessity of explainability in diagnostic processes. This review utilizes multi-modal data and emphasizes explainability to enhance diagnostic accuracy, clinician confidence, and patient engagement, ultimately fostering more personalized treatment strategies for breast cancer, while also identifying research gaps in multi-modality and explainability, guiding future studies, and contributing to the strategic direction of the field.

FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures

Retinal vascular segmentation, is a widely researched subject in biomedical image processing, aims to relieve ophthalmologists' workload when treating and detecting retinal disorders. However, segmenting retinal vessels has its own set of challenges, with prior techniques failing to generate adequate results when segmenting branches and microvascular structures. The neural network approaches used recently are characterized by the inability to keep local and global properties together and the failure to capture tiny end vessels make it challenging to attain the desired result. To reduce this retinal vessel segmentation problem, we propose a full-scale micro-vessel extraction mechanism based on an encoder-decoder neural network architecture, sigmoid smoothing, and an adaptive threshold method. The network consists of of residual, encoder booster, bottleneck enhancement, squeeze, and excitation building blocks. All of these blocks together help to improve the feature extraction and prediction of the segmentation map. The proposed solution has been evaluated using the DRIVE, CHASE-DB1, and STARE datasets, and competitive results are obtained when compared with previous studies. The AUC and accuracy on the DRIVE dataset are 0.9884 and 0.9702, respectively. On the CHASE-DB1 dataset, the scores are 0.9903 and 0.9755, respectively. On the STARE dataset, the scores are 0.9916 and 0.9750, respectively. The performance achieved is one step ahead of what has been done in previous studies, and this results in a higher chance of having this solution in real-life diagnostic centers that seek ophthalmologists attention.