Composed Image Retrieval via Cross Relation Network With Hierarchical Aggregation Transformer.

Composing Text and Image to Image Retrieval (CTI-IR) aims at finding the target image, which matches the query image visually along with the query text semantically. However, existing works ignore the fact that the reference text usually serves multiple functions, e.g.

Cross-Modality Pyramid Alignment for Visual Intention Understanding.

Visual intention understanding is the task of exploring the potential and underlying meaning expressed in images. Simply modeling the objects or backgrounds within the image content leads to unavoidable comprehension bias. To alleviate this problem, this paper proposes a Cross-modality Pyramid Alignment with Dynamic optimization (CPAD) to enhance the global understanding of visual intention with hierarchical modeling.

Deep learning assisted contrast-enhanced CT-based diagnosis of cervical lymph node metastasis of oral cancer: a retrospective study of 1466 cases.

Objectives: Lymph node (LN) metastasis is a common cause of recurrence in oral cancer; however, the accuracy of distinguishing positive and negative LNs is not ideal. Here, we aimed to develop a deep learning model that can identify, locate, and distinguish LNs in contrast-enhanced CT (CECT) images with a higher accuracy.

Methods: The preoperative CECT images and corresponding postoperative pathological diagnoses of 1466 patients with oral cancer from our hospital were retrospectively collected.

Burn Images Segmentation Based on Burn-GAN.

Burn injuries are severe problems for human. Accurate segmentation for burn wounds in patient surface can improve the calculation precision of %TBSA (total burn surface area), which is helpful in determining treatment plan. Recently, deep learning methods have been used to automatically segment wounds.

Burn image segmentation based on Mask Regions with Convolutional Neural Network deep learning framework: more accurate and more convenient.

Background: Burns are life-threatening with high morbidity and mortality. Reliable diagnosis supported by accurate burn area and depth assessment is critical to the success of the treatment decision and, in some cases, can save the patient's life. Current techniques such as straight-ruler method, aseptic film trimming method, and digital camera photography method are not repeatable and comparable, which lead to a great difference in the judgment of burn wounds and impede the establishment of the same evaluation criteria.