Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound is a new approach for breast screening, which has many advantages over handheld mammography, such as safety, speed, and higher detection rate of breast cancer in dense breasts. Thus it could prevail over the world in next several years. Tumor detection, segmentation and classification are three basic tasks in medical image analysis. These tasks are very challenging on 3D ABUS volumes for large tumor size and shape variation, irregular and ambiguous tumor boundaries, and low signal-to-noise ratio. Furthermore, there are rare open accessible ABUS datasets with well labeled tumor, which hinder the development of breast tumor detection, segmentation and classification systems. Thus, we try to host the first Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound 2023 (Named TDSC-ABUS2023) to start a new research topic and make a solid benchmark for 3D ABUS image detection, segmentation and classification tasks. We have collected 400 3D volumes with refined both tumor boundaries and categories labeling from 10 experienced radiologists , 200 for the training dataset, 140 for the closed testing dataset and 60 for the opened validated dataset. Dice, HD, are adopted as evaluation metrics for segmentation, balanced accuracy F1-score and AUC are used as evaluation metrics for classification and mAP@0.75 is taken for detection. This challenge will also promote the breast cancer treatment, interactions between researchers and interdisciplinary communication.