Function generate_imaging_diagnosis

code/inference/eval.py:298–355

(example)

def generate_imaging_diagnosis(example):
    clinical_case_uid = example['clinical_case_uid']
    language = example['language']
    imageological_examination = example['imageological_examination']

    logging.info('===> [imaging diagnosis inference]...')
    logging.info('### [clinical case uid]: ' + str(clinical_case_uid))

    if isinstance(imageological_examination, dict):
        for imageological_examination_part_feature in imageological_examination.keys():
            findings = imageological_examination[imageological_examination_part_feature]['findings']
            if language == 'zh':
                imageological_examination_part_name = imageological_examination_part_feature_to_name_zh_dict[imageological_examination_part_feature]
                prompt = f'''您是一位经验丰富的放射科医生，根据给定的{imageological_examination_part_name}检查报告中的影像所见部分，请分析并分条列出专业的影像诊断。
请确保您的回答高度简洁。

以下是给定的影像所见：
{findings}

您提供的影像诊断：'''
            else:
                imageological_examination_part_name = imageological_examination_part_feature_to_name_en_dict[imageological_examination_part_feature]
                prompt = f'''You are an experienced radiologist. Based on the findings section of the given {imageological_examination_part_name} examination report, please analyze and list professional impression.
Please ensure that your response is highly concise.

The following is the given findings:
{findings}

Your impression:'''
            prompt_tokens = evaluator.count_tokens(prompt)

            logging.info('### [prompt]: ' + str(prompt))
            logging.info('### [prompt tokens]: ' + str(prompt_tokens))

            try:
                response = evaluator.generate_text(prompt)

                logging.info('### [response]: ' + str(response))

                if response is None:
                    response = ''
                    response_tokens = 0
                else:
                    response_tokens = evaluator.count_tokens(response)
                total_tokens = prompt_tokens + response_tokens

                logging.info('### [response tokens]: ' + str(response_tokens))
                logging.info('### [total tokens]: ' + str(total_tokens))

            except Exception as e:
                response = ''

                logging.error('### [Failed to generate text]: ' + e.__str__())

            imageological_examination[imageological_examination_part_feature]['prompt_impression'] = str(evaluator.format_prompt(prompt))
            imageological_examination[imageological_examination_part_feature]['predicted_impression'] = response

    return example